%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%% % % % The Complete Bibliography of the ``Information Retrieval'' journal % % Kluwer Academic Publishers % % % % compiled and maintained by % % % % Fabrizio Sebastiani % % Dipartimento di Matematica Pura e Applicata % % Universita' di Padova % % Via Giovanni Battista Belzoni, 7 - 35131 Padova, Italy % % http://www.math.unipd.it/~fabseb60/ % % % %%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%% %%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%% % % % Volume 1, Issue 1/2, 1999 % % % %%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%% @article{Kantor99, author = {Paul Kantor and Steve Robertson}, title = {Editorial}, journal = {Information Retrieval}, publisher = {Kluwer Academic Publishers}, issn = {1386-4564}, number = {1/2}, volume = {1}, pages = {5}, year = {1999}, url = {http://www.kluweronline.com/issn/1386-4564}, abstract = {}, } @article{Banks99, author = {David Banks and Paul Over and Nien-Fan Zhang}, title = {Blind Men and Elephants: Six Approaches to {TREC} data}, journal = {Information Retrieval}, publisher = {Kluwer Academic Publishers}, issn = {1386-4564}, number = {1/2}, volume = {1}, pages = {7--34}, year = {1999}, url = {http://www.kluweronline.com/issn/1386-4564}, abstract = {The paper reviews six recent efforts to better understand performance measurements on information retrieval (IR) systems within the framework of the Text REtrieval Conferences (TREC): analysis of variance, cluster analyses, rank correlations, beadplots, multidimensional scaling, and item response analysis. None of this work has yielded any substantial new insights. Prospects that additional work along these lines will yield more interesting results vary but are in general not promising. Some suggestions are made for paying greater attention to richer descriptions of IR system behavior but within smaller, better controlled settings.}, } @article{Mani99, author = {Inderjeet Mani and Eric Bloedorn}, title = {Summarizing Similarities and Differences Among Related Documents}, journal = {Information Retrieval}, publisher = {Kluwer Academic Publishers}, issn = {1386-4564}, number = {1/2}, volume = {1}, pages = {35--67}, year = {1999}, url = {http://www.kluweronline.com/issn/1386-4564}, abstract = {In many modern information retrieval applications, a common problem which arises is the existence of multiple documents covering similar information, as in the case of multiple news stories about an event or a sequence of events. A particular challenge for text summarization is to be able to summarize the similarities and differences in information content among these documents. The approach described here exploits the results of recent progress in information extraction to represent salient units of text and their relationships. By exploiting meaningful relations between units based on an analysis of text cohesion and the context in which the comparison is desired, the summarizer can pinpoint similarities and differences, and align text segments. In evaluation experiments, these techniques for exploiting cohesion relations result in summaries which (i) help users more quickly complete a retrieval task (ii) result in improved alignment accuracy over baselines, and (iii) improve identification of topic-relevant similarities and differences.}, } @article{Yang99, author = {Yiming Yang}, title = {An Evaluation of Statistical Approaches to Text Categorization}, journal = {Information Retrieval}, publisher = {Kluwer Academic Publishers}, issn = {1386-4564}, number = {1/2}, volume = {1}, pages = {69--90}, year = {1999}, url = {http://www.kluweronline.com/issn/1386-4564}, abstract = {This paper focuses on a comparative evaluation of a wide-range of text categorization methods, including previously published results on the Reuters corpus and new results of additional experiments. A controlled study using three classifiers, kNN, LLSF and WORD, was conducted to examine the impact of configuration variations in five versions of Reuters on the observed performance of classifiers. Analysis and empirical evidence suggest that the evaluation results on some versions of Reuters were significantly affected by the inclusion of a large portion of unlabelled documents, mading those results difficult to interpret and leading to considerable confusions in the literature. Using the results evaluated on the other versions of Reuters which exclude the unlabelled documents, the performance of twelve methods are compared directly or indirectly. For indirect compararions, kNN, LLSF and WORD were used as baselines, since they were evaluated on all versions of Reuters that exclude the unlabelled documents. As a global observation, kNN, LLSF and a neural network method had the best performance; except for a Naive Bayes approach, the other learning algorithms also performed relatively well.}, } @article{Melucci99, author = {Massimo Melucci}, title = {An Evaluation of Automatically Constructed Hypertexts for Information Retrieval}, journal = {Information Retrieval}, publisher = {Kluwer Academic Publishers}, issn = {1386-4564}, number = {1/2}, volume = {1}, pages = {91--114}, year = {1999}, url = {http://www.kluweronline.com/issn/1386-4564}, abstract = {This paper assesses the retrieval effectiveness of automatically constructed inter-document hypertext links in Information Retrieval (IR). The objectives of the experiments described are to obtain evidence concerning the usefulness of querying and browsing automatically constructed IR hypertexts. Links are built by using IR techniques, as these enable rapid, automatic production of hypertexts from a document collection for accessing the collection itself. These tests are carried out in a laboratory environment and through simulation of link browsing. Results of experiments show that browsing has little impact on the retrieval of relevant documents if used in place of querying or relevance feedback methods, though may be practical if used in combination with them.}, } @article{Hawking99, author = {David Hawking and Paul Thistlewaite and Donna Harman}, title = {Scaling Up the {TREC} Collection}, journal = {Information Retrieval}, publisher = {Kluwer Academic Publishers}, issn = {1386-4564}, number = {1/2}, volume = {1}, pages = {115--137}, year = {1999}, url = {http://www.kluweronline.com/issn/1386-4564}, abstract = {Due to the popularity of Web search engines, a large proportion of real text retrieval queries are now processed over collections measured in tens or hundreds of gigabytes. A new Very Large test Collection (VLC) has been created to support qualification, measurement and comparison of systems operating at this level and to permit the study of the properties of very large collections. The VLC is an extension of the well-known TREC collection and has been distributed under the same conditions. A simple set of efficiency and effectiveness measures have been defined to encourage comparability of reporting. The 20 gigabyte first-edition of the VLC and a representative 10\% sample have been used in a special interest track of the 1997 Text Retrieval Conference (TREC-6). The unaffordable cost of obtaining complete relevance assessments over collections of this scale is avoided by concentrating on early precision and relying on the core TREC collection to support detailed effectiveness studies. Results obtained by TREC-6 VLC track participants are presented here. All groups observed a significant increase in early precision as collection size increased. Explanatory hypotheses are advanced for future empirical testing. A 100 gigabyte second edition VLC (VLC2) has recently been compiled and distributed for use in TREC-7 in 1998.}, } @article{Vogt99, author = {Christopher C. Vogt and Garrison W. Cottrell}, title = {Fusion Via a Linear Combination of Scores}, journal = {Information Retrieval}, publisher = {Kluwer Academic Publishers}, issn = {1386-4564}, number = {3}, volume = {1}, pages = {151--173}, year = {1999}, url = {http://www.kluweronline.com/issn/1386-4564}, abstract = {We present a thorough analysis of the capabilities of the linear combination (LC) model for fusion of information retrieval systems. The LC model combines the results lists of multiple IR systems by scoring each document using a weighted sum of the scores from each of the component systems. We first present both empirical and analytical justification for the hypotheses that such a model should only be used when the systems involved have high performance, a large overlap of relevant documents, and a small overlap of nonrelevant documents. The empirical approach allows us to very accurately predict the performance of a combined system. We also derive a formula for a theoretically optimal weighting scheme for combining 2 systems. We introduce dÑthe difference between the average score on relevant documents and the average score on nonrelevant documentsÑas a performance measure which not only allows mathematical reasoning about system performance, but also allows the selection of weights which generalize well to new documents. We describe a number of experiments involving large numbers of different IR systems which support these findings.}, } @article{Boughanem99, author = {M. Boughanem and C. Chrisment and L. Tamine}, title = {Genetic Approach to Query Space Exploration}, journal = {Information Retrieval}, publisher = {Kluwer Academic Publishers}, issn = {1386-4564}, number = {3}, volume = {1}, pages = {175--192}, year = {1999}, url = {http://www.kluweronline.com/issn/1386-4564}, abstract = {This paper describes a genetic algorithm approach for intelligent information retrieval. The goal is to find an optimal set of documents which best matches the user's needs by exploring and exploiting the document space. More precisely, we define a specific genetic algorithm for information retrieval based on knowledge based operators and guided by a heuristic for relevance multi-modality problem solving. Experiments with TREC-6 French data and queries show the effectiveness of our approach.}, } @article{Weigend99, author = {Andreas S. Weigend and Erik D. Wiener and Jan O. Pedersen}, title = {Exploiting Hierarchy in Text Categorization}, journal = {Information Retrieval}, publisher = {Kluwer Academic Publishers}, issn = {1386-4564}, number = {3}, volume = {1}, pages = {193--216}, year = {1999}, url = {http://www.kluweronline.com/issn/1386-4564}, abstract = {With the recent dramatic increase in electronic access to documents, text categorizationÑthe task of assigning topics to a given documentÑhas moved to the center of the information sciences and knowledge management. This article uses the structure that is present in the semantic space of topics in order to improve performance in text categorization: according to their meaning, topics can be grouped together into ``meta-topics'', e.g., gold, silver, and copper are all metals. The proposed architecture matches the hierarchical structure of the topic space, as opposed to a flat model that ignores the structure. It accommodates both single and multiple topic assignments for each document. Its probabilistic interpretation allows its predictions to be combined in a principled way with information from other sources. The first level of the architecture predicts the probabilities of the meta-topic groups. This allows the individual models for each topic on the second level to focus on finer discriminations within the group. Evaluating the performance of a two-level implementation on the Reuters-22173 testbed of newswire articles shows the most significant improvement for rare classes.}, } @article{Pirkola99, author = {Ari Pirkola and Heikki Keskustalo and Kalervo J{\"{a}}rvelin}, title = {The Effects of Conjunction, Facet Structure, and Dictionary Combinations in Concept-Based Cross-Language Retrieval}, journal = {Information Retrieval}, publisher = {Kluwer Academic Publishers}, issn = {1386-4564}, number = {3}, volume = {1}, pages = {217--250}, year = {1999}, url = {http://www.kluweronline.com/issn/1386-4564}, abstract = {The paper studies concept-based cross-language information retrieval (CLIR). The document collection was a subset of the TREC collection. The test requests were formed from TREC's health related topics. As translation dictionaries the study used a general dictionary and a domain-specific (=medical) dictionary. The effects of translation method, conjunction, and facet order on the effectiveness of concept-based cross-language queries were studied, and concept-based structuring of cross-language queries was compared to mechanical structuring based on the output of dictionaries. The performance of translated Finnish queries against English documents was compared to the performance of original English queries against the English documents, and the performance of different CLIR query types was compared with one another. No major difference was found between concept-based and mechanical structuring. The best translation method was a simultaneous look-up in the medical dictionary and the general dictionary, in which case cross-language queries performed as well as the original English queries. The results showed that especially at high exhaustivity (the number of mutually restrictive concepts in a request) levels cross-language queries perform well in relation to monolingual queries. This suggests that conjunction disambiguates cross-language queries. An extensive study was made of the relative importance of the concepts of requests. On the basis of the classification data of request concepts it was shown how the order of facets in a query affects cross-language as well as monolingual queries.}, } @article{Markkula00, author = {Marjo Markkula and Eero Sormunen}, title = {End-User Searching Challenges Indexing Practices in the Digital Newspaper Photo Archive}, journal = {Information Retrieval}, publisher = {Kluwer Academic Publishers}, issn = {1386-4564}, number = {4}, volume = {1}, pages = {259--285}, year = {2000}, url = {http://www.kluweronline.com/issn/1386-4564}, abstract = {Previous research in conceptual indexing methods of images has furnished us with refined theoretical frameworks characterising various aspects of images that could and should be indexed using textual descriptors. The development of digital image processing technologies has bred a brigade of content-based indexing and retrieval methods available for applications. What the users need and in what kinds of environments different indexing and retrieval methods are relevant, has remained an area of less intensive research work.This article presents the results of a field study concentrating on journalists as users of a digital newspaper photo archive. The expressed photo needs, applied selection criteria and observed searching behaviours in journalists' daily work were contrasted with the indexing practices applied by the archivists. The results showed that the journalists achieved satisfactory results when trivial query terms were available, e.g. when photos of named persons were needed. Browsing was the main searching strategy applied by the journalists, but the system did not support browsing well. The access problems faced by the users in particular photo needs are discussed in detail. The paper concludes by discussing the potential approaches in developing both the concept-based and content-based indexing methods as well as the user interfaces in photo retrieval systems.}, } @article{Frost00, author = {C. Olivia Frost and Bradley Taylor and Anna Noakes and Stephen Markel and Deborah Torres and Karen M. Drabenstott}, title = {Browse and Search Patterns in a Digital Image Database}, journal = {Information Retrieval}, publisher = {Kluwer Academic Publishers}, issn = {1386-4564}, number = {4}, volume = {1}, pages = {287--313}, year = {2000}, url = {http://www.kluweronline.com/issn/1386-4564}, abstract = {A prototype image retrieval system with browse and search capabilities was developed to investigate patterns of searching a collection of digital visual images, as well as factors, such as image size, resolution, and download speed, which affect browsing. The subject populations were art history specialists and non-specialists. Through focus group interviews, a controlled test, post-test interviews and an online survey, data was gathered to compare preferences and actual patterns of use in browsing and searching. While specialists preferred direct search to browsing, and generalists used browsing as their preferred mode, both user groups found each mode to play a role depending on information need, and found value in a system combining both browse and direct search. There were no significant differences in performance among the search modes of browse, search, and combined browse/search models when the quasi-controlled study tested the different modes.}, } @article{Bookstein00, author = {A. Bookstein and S.T. Klein and T. Raita}, title = {Simple {Bayesian} Model for Bitmap Compression}, journal = {Information Retrieval}, publisher = {Kluwer Academic Publishers}, issn = {1386-4564}, number = {4}, volume = {1}, pages = {315--328}, year = {2000}, url = {http://www.kluweronline.com/issn/1386-4564}, abstract = {Bitmaps are a useful, but storage voracious, component of many information retrieval systems. Earlier efforts to compress bitmaps were based on models of bit generation, particularly Markov models. While these permitted considerable reduction in storage, the short memory of Markov models may limit their compression efficiency. In this paper we accept the state orientation of Markov models, but introduce a Bayesian approach to assess the state; the analysis is based on data accumulating in a growing window. The paper describes the details of the probabilistic assumptions governing the Bayesian analysis, as well as the protocol for controlling the window that receives the data. We find slight improvement over the best performing strictly Markov models.}, } @article{Kekalainen00, author = {Jaana Kek{\"{a}}l{\"{a}}inen and Kalervo J{\"{a}}rvelin}, title = {The Co-Effects of Query Structure and Expansion on Retrieval Performance in Probabilistic Text Retrieval}, journal = {Information Retrieval}, publisher = {Kluwer Academic Publishers}, issn = {1386-4564}, number = {4}, volume = {1}, pages = {329--344}, year = {2000}, url = {http://www.kluweronline.com/issn/1386-4564}, abstract = {The effects of query structures and query expansion (QE) on retrieval performance were tested with a best match retrieval system (InQuery). Query structure means the use of operators to express the relations between search keys. Six different structures were tested, representing strong structures (e.g., queries with facets or concepts identified) and weak structures (no concepts identified, a query is 'a bag of search keys'). QE was based on concepts, which were first selected from a searching thesaurus, and then expanded by semantic relationships given in the thesaurus. The expansion levels were (a) no expansion, (b) a synonym expansion, (c) a narrower concept expansion, (d) an associative concept expansion, and (e) a cumulative expansion of all other expansions. With weak structures and Boolean structured queries, QE was not very effective. The best performance was achieved with a combination of a facet structure, where search keys within a facet were treated as instances of one search key (the SYN operator), and the largest expansion.}, } @article{Dunlop00, author = {Mark Dunlop and Mounia Lalmas}, title = {Introduction}, journal = {Information Retrieval}, publisher = {Kluwer Academic Publishers}, issn = {1386-4564}, number = {1}, volume = {2}, pages = {9--15}, year = {2000}, url = {http://www.kluweronline.com/issn/1386-4564}, abstract = {}, } @article{Rijsbergen00, author = {C.J. Van Rijsbergen}, title = {Another Look at the Logical Uncertainty Principle}, journal = {Information Retrieval}, publisher = {Kluwer Academic Publishers}, issn = {1386-4564}, number = {1}, volume = {2}, pages = {17--26}, year = {2000}, url = {http://www.kluweronline.com/issn/1386-4564}, abstract = {The Logical Uncertainty Principle is re-examined from the point of classical logic. Two interpretations are given, an objective one in terms of an axiomatic theory of information, and a subjective one based on Ramsey's theory of probability.}, } @article{Crestani00, author = {Fabio Crestani}, title = {Exploiting the Similarity of Non-Matching Terms at Retrieval Time}, journal = {Information Retrieval}, publisher = {Kluwer Academic Publishers}, issn = {1386-4564}, number = {1}, volume = {2}, pages = {27--47}, year = {2000}, url = {http://www.kluweronline.com/issn/1386-4564}, abstract = {In classic Information Retrieval systems a relevant document will not be retrieved in response to a query if the document and query representations do not share at least one term. This problem, known as ``term mismatch'', has been recognised for a long time by the Information Retrieval community and a number of possible solutions have been proposed. Here I present a preliminary investigation into a new class of retrieval models that attempt to solve the term mismatch problem by exploiting complete or partial knowledge of term similarity in the term space. The use of term similarity enables to enhance classic retrieval models by taking into account non-matching terms. The theoretical advantages and drawbacks of these models are presented and compared with other models tackling the same problem. A preliminary experimental investigation into the performance gain achieved by exploiting term similarity with the proposed models is presented and discussed.}, } @article{Sanderson00, author = {Mark Sanderson}, title = {Retrieving with Good Sense}, journal = {Information Retrieval}, publisher = {Kluwer Academic Publishers}, issn = {1386-4564}, number = {1}, volume = {2}, pages = {49--69}, year = {2000}, url = {http://www.kluweronline.com/issn/1386-4564}, abstract = {Although always present in text, word sense ambiguity only recently became regarded as a problem to information retrieval which was potentially solvable. The growth of interest in word senses resulted from new directions taken in disambiguation research. This paper first outlines this research and surveys the resulting efforts in information retrieval. Although the majority of attempts to improve retrieval effectiveness were unsuccessful, much was learnt from the research. Most notably a notion of under what circumstance disambiguation may prove of use to retrieval.}, } @article{Adriani00, author = {Mirna Adriani}, title = {Using Statistical Term Similarity for Sense Disambiguation in Cross-Language Information Retrieval}, journal = {Information Retrieval}, publisher = {Kluwer Academic Publishers}, issn = {1386-4564}, number = {1}, volume = {2}, pages = {71--82}, year = {2000}, url = {http://www.kluweronline.com/issn/1386-4564}, abstract = {With the increasing availability of machine-readable bilingual dictionaries, dictionary-based automatic query translation has become a viable approach to Cross-Language Information Retrieval (CLIR). In this approach, resolving term ambiguity is a crucial step. We propose a sense disambiguation technique based on a term-similarity measure for selecting the right translation sense of a query term. In addition, we apply a query expansion technique which is also based on the term similarity measure to improve the effectiveness of the translation queries. The results of our Indonesian to English and English to Indonesian CLIR experiments demonstrate the effectiveness of the sense disambiguation technique. As for the query expansion technique, it is shown to be effective as long as the term ambiguity in the queries has been resolved. In the effort to solve the term ambiguity problem, we discovered that differences in the pattern of word-formation between the two languages render query translations from one language to the other difficult.}, } @article{Ruthven00, author = {Ian Ruthven}, title = {Incorporating Aspects of Information Use into Relevance Feedback}, journal = {Information Retrieval}, publisher = {Kluwer Academic Publishers}, issn = {1386-4564}, number = {1}, volume = {2}, pages = {83--88}, year = {2000}, url = {http://www.kluweronline.com/issn/1386-4564}, abstract = {In this paper we look at some of the problems in interacting with best-match retrieval systems. In particular, we examine the areas of interaction, some investigations of the complexity and breadth of interaction and attempts to categorise user's information seeking behaviour. We suggest that one of the difficulties of traditional IR systems in supporting information seeking is the way the information content of documents is represented. We discuss an alternative representation, based on how information is used within documents.}, } @article{Campbell00, author = {Iain Campbell}, title = {Interactive Evaluation of the Ostensive Model Using a New Test Collection of Images with Multiple Relevance Assessments}, journal = {Information Retrieval}, publisher = {Kluwer Academic Publishers}, issn = {1386-4564}, number = {1}, volume = {2}, pages = {89--114}, year = {2000}, url = {http://www.kluweronline.com/issn/1386-4564}, abstract = {The Ostensive Model proposes a manner of structuring the uncertainty associated with individual relevance judgements as sources of evidence in relevance feedback. It proposes temporal profiles of uncertainty, motivating the application of a particular class of discount function with respect to the age of the evidence. This paper presents an initial evaluation of the relative effectiveness of different uncertainty discount functions.A novel direct manipulation interface to a multimedia retrieval system embodying the Ostensive Model is outlined briefly. The paper describes the construction and characteristics of a new image test collection utilising multiple binary relevance assessments. The use of such multiple assessments and multiple interpretations of them are discussed. The evaluation environment is detailed in terms of the interface, test collection, and tasks set to users. Multiple interpretations of the results, and the statistical significance of comparisons are presented.The results obtained in the evaluation are consistent with the proposals of the Ostensive ModelÑreinforcing a particular evidence profile. The results give clear pointers to further, more specific, evaluations.}, } @article{Reid00, author = {Jane Reid}, title = {A Task-Oriented Non-Interactive Evaluation Methodology for Information Retrieval Systems}, journal = {Information Retrieval}, publisher = {Kluwer Academic Publishers}, issn = {1386-4564}, number = {1}, volume = {2}, pages = {115--129}, year = {2000}, url = {http://www.kluweronline.com/issn/1386-4564}, abstract = {Past research has identified many different types of relevance in information retrieval (IR). So far, however, most evaluation of IR systems has been through batch experiments conducted with test collections containing only expert, topical relevance judgements. Recently, there has been some movement away from this traditional approach towards interactive, more user-centred methods of evaluation. However, these are expensive for evaluators in terms both of time and of resources. This paper describes a new evaluation methodology, using a task-oriented test collection, which combines the advantages of traditional non-interactive testing with a more user-centred emphasis. The main features of a task-oriented test collection are the adoption of the task, rather than the query, as the primary unit of evaluation and the naturalistic character of the relevance judgements.}, } @article{Baird00, author = {Henry S. Baird and Francine R. Chen}, title = {Introduction}, journal = {Information Retrieval}, publisher = {Kluwer Academic Publishers}, issn = {1386-4564}, number = {2/3}, volume = {2}, pages = {139--140}, year = {2000}, url = {http://www.kluweronline.com/issn/1386-4564}, abstract = {}, } @article{Mitra00, author = {M. Mitra and B.B. Chaudhuri}, title = {Information Retrieval from Documents: A Survey}, journal = {Information Retrieval}, publisher = {Kluwer Academic Publishers}, issn = {1386-4564}, number = {2/3}, volume = {2}, pages = {141--163}, year = {2000}, url = {http://www.kluweronline.com/issn/1386-4564}, abstract = {Given the phenomenal growth in the variety and quantity of data available to users through electronic media, there is a great demand for efficient and effective ways to organize and search through all this information. Besides speech, our principal means of communication is through visual media, and in particular, through documents. In this paper, we provide an update on Doermann's comprehensive survey (1998) of research results in the broad area of document-based information retrieval. The scope of this survey is also somewhat broader, and there is a greater emphasis on relating document image analysis methods to conventional IR methods.Documents are available in a wide variety of formats. Technical papers are often available as ASCII files of clean, correct, text. Other documents may only be available as hardcopies. These documents have to be scanned and stored as images so that they may be processed by a computer. The textual content of these documents may also be extracted and recognized using OCR methods. Our survey covers the broad spectrum of methods that are required to handle different formats like text and images. The core of the paper focuses on methods that manipulate document images directly, and perform various information processing tasks such as retrieval, categorization, and summarization, without attempting to completely recognize the textual content of the document. We start, however, with a brief overview of traditional IR techniques that operate on clean text. We also discuss research dealing with text that is generated by running OCR on document images. Finally, we also briefly touch on the related problem of content-based image retrieval.}, } @article{Kantor00, author = {Paul B. Kantor and Ellen M. Voorhees}, title = {The {TREC-5} Confusion Track: Comparing Retrieval Methods for Scanned Text}, journal = {Information Retrieval}, publisher = {Kluwer Academic Publishers}, issn = {1386-4564}, number = {2/3}, volume = {2}, pages = {165--176}, year = {2000}, url = {http://www.kluweronline.com/issn/1386-4564}, abstract = {A known-item search is a particular information retrieval task in which the system is asked to find a single target document in a large document set. The TREC-5 confusion track used a set of 49 known-item tasks to study the impact of data corruption on retrieval system performance. Two corrupted versions of a 55,600 document corpus whose true content was known were created by applying OCR techniques to page images. The first version of the corpus used the page images as scanned, resulting in an estimated character error rate of approximately 5\%. The second version used page images that had been down-sampled, resulting in an estimated character error rate of approximately 20\%. The true text and each of the corrupted versions were then searched using the same set of 49 questions. In general, retrieval methods that attempted a probabilistic reconstruction of the original clean text fared better than methods that simply accepted corrupted versions of the query text.}, } @article{Lopresti00, author = {Daniel Lopresti and Jiangying Zhou}, title = {Locating and Recognizing Text in {WWW} Images}, journal = {Information Retrieval}, publisher = {Kluwer Academic Publishers}, issn = {1386-4564}, number = {2/3}, volume = {2}, pages = {177--206}, year = {2000}, url = {http://www.kluweronline.com/issn/1386-4564}, abstract = {The explosive growth of the World Wide Web has resulted in a distributed database consisting of hundreds of millions of documents. While existing search engines index a page based on the text that is readily extracted from its HTML encoding, an increasing amount of the information on the Web is embedded in images. This situation presents a new and exciting challenge for the fields of document analysis and information retrieval, as WWW image text is typically rendered in color and at very low spatial resolutions. In this paper, we survey the results of several years of our work in the area. For the problem of locating text in Web images, we describe a procedure based on clustering in color space followed by a connected-components analysis that seems promising. For character recognition, we discuss techniques using polynomial surface fitting and ``fuzzy'' n-tuple classifiers. Also presented are the results of several experiments that demonstrate where our methods perform well and where more work needs to be done. We conclude with a discussion of topics for further research.}, } @article{Williams00, author = {William J. Williams and Eugene J. Zalubas and Alfred O. Hero, {III}}, title = {Word Spotting in Bitmapped Fax Documents}, journal = {Information Retrieval}, publisher = {Kluwer Academic Publishers}, issn = {1386-4564}, number = {2/3}, volume = {2}, pages = {207--226}, year = {2000}, url = {http://www.kluweronline.com/issn/1386-4564}, abstract = {Images and signals may be represented by forms invariant to time shifts, spatial shifts, frequency shifts, and scale changes. Advances in time-frequency analysis and scale transform techniques have made this possible. However, factors such as noise contamination and ``style'' differences complicate this. An example is found in text, where letters and words may vary in size and position. Examples of complicating variations include the font used, corruption during facsimile (fax) transmission, and printer characteristics. The solution advanced in this paper is to cast the desired invariants into separate subspaces for each extraneous factor or group of factors. The first goal is to have minimal overlap between these subspaces and the second goal is to be able to identify each subspace accurately. Concepts borrowed from high-resolution spectral analysis, but adapted uniquely to this problem have been found to be useful in this context. Once the pertinent subspace is identified, the recognition of a particular invariant form within this subspace is relatively simple using well-known singular value decomposition (SVD) techniques. The basic elements of the approach can be applied to a variety of pattern recognition problems. The specific application covered in this paper is word spotting in bitmapped fax documents.}, } @article{Hu00, author = {Jianying Hu and Ramanujan Kashi and Gordon Wilfong}, title = {Comparison and Classification of Documents Based on Layout Similarity}, journal = {Information Retrieval}, publisher = {Kluwer Academic Publishers}, issn = {1386-4564}, number = {2/3}, volume = {2}, pages = {227--243}, year = {2000}, url = {http://www.kluweronline.com/issn/1386-4564}, abstract = {This paper describes features and methods for document image comparison and classification at the spatial layout level. The methods are useful for visual similarity based document retrieval as well as fast algorithms for initial document type classification without OCR. A novel feature set called interval encoding is introduced to capture elements of spatial layout. This feature set encodes region layout information in fixed-length vectors by capturing structural characteristics of the image. These fixed-length vectors are then compared to each other through a Manhattan distance computation for fast page layout comparison. The paper describes experiments and results to rank-order a set of document pages in terms of their layout similarity to a test document. We also demonstrate the usefulness of the features derived from interval coding in a hidden Markov model based page layout classification system that is trainable and extendible. The methods described in the paper can be used in various document retrieval tasks including visual similarity based retrieval, categorization and information extraction.}, } @article{Srihari00, author = {Rohini K. Srihari and Zhongfei Zhang and Aibing Rao}, title = {Intelligent Indexing and Semantic Retrieval of Multimodal Documents}, journal = {Information Retrieval}, publisher = {Kluwer Academic Publishers}, issn = {1386-4564}, number = {2/3}, volume = {2}, pages = {245--275}, year = {2000}, url = {http://www.kluweronline.com/issn/1386-4564}, abstract = {Finding useful information from large multimodal document collections such as the WWW without encountering numerous false positives poses a challenge to multimedia information retrieval systems (MMIR). This research addresses the problem of finding pictures. The fact that images do not appear in isolation, but rather with accompanying, collateral text is exploited. Taken independently, existing techniques for picture retrieval using (i) text-based and (ii) image-based methods have several limitations. This research presents a general model for multimodal information retrieval that addresses the following issues: (i) users' information need, (ii) expressing information need through composite, multimodal queries, and (iii) determining the most appropriate weighted combination of indexing techniques in order to best satisfy information need. A machine learning approach is proposed for the latter. The focus is on improving precision and recall in a MMIR system by optimally combining text and image similarity. Experiments are presented which demonstrate the utility of individual indexing systems in improving overall average precision.}, } @article{Hughey00, author = {M.K. Hughey and M.W. Berry}, title = {Improved Query Matching Using kd-Trees: A Latent Semantic Indexing Enhancement}, journal = {Information Retrieval}, publisher = {Kluwer Academic Publishers}, issn = {1386-4564}, number = {4}, volume = {2}, pages = {287--302}, year = {2000}, url = {http://www.kluweronline.com/issn/1386-4564}, abstract = {Efficient information searching and retrieval methods are needed to navigate the ever increasing volumes of digital information. Traditional lexical information retrieval methods can be inefficient and often return inaccurate results. To overcome problems such as polysemy and synonymy, concept-based retrieval methods have been developed. One such method is Latent Semantic Indexing (LSI), a vector-space model, which uses the singular value decomposition (SVD) of a term-by-document matrix to represent terms and documents in k-dimensional space. As with other vector-space models, LSI is an attempt to exploit the underlying semantic structure of word usage in documents. During the query matching phase of LSI, a user's query is first projected into the term-document space, and then compared to all terms and documents represented in the vector space. Using some similarity measure, the nearest (most relevant) terms and documents are identified and returned to the user. The current LSI query matching method requires that the similarity measure be computed between the query and every term and document in the vector space. In this paper, the kd-tree searching algorithm is used within a recent LSI implementation to reduce the time and computational complexity of query matching. The kd-tree data structure stores the term and document vectors in such a way that only those terms and documents that are most likely to qualify as nearest neighbors to the query will be examined and retrieved.}, } @article{Turney00, author = {Peter D. Turney}, title = {Learning Algorithms for Keyphrase Extraction}, journal = {Information Retrieval}, publisher = {Kluwer Academic Publishers}, issn = {1386-4564}, number = {4}, volume = {2}, pages = {303--336}, year = {2000}, url = {http://www.kluweronline.com/issn/1386-4564}, abstract = {Many academic journals ask their authors to provide a list of about five to fifteen keywords, to appear on the first page of each article. Since these key words are often phrases of two or more words, we prefer to call them keyphrases. There is a wide variety of tasks for which keyphrases are useful, as we discuss in this paper. We approach the problem of automatically extracting keyphrases from text as a supervised learning task. We treat a document as a set of phrases, which the learning algorithm must learn to classify as positive or negative examples of keyphrases. Our first set of experiments applies the C4.5 decision tree induction algorithm to this learning task. We evaluate the performance of nine different configurations of C4.5. The second set of experiments applies the GenEx algorithm to the task. We developed the GenEx algorithm specifically for automatically extracting keyphrases from text. The experimental results support the claim that a custom-designed algorithm (GenEx), incorporating specialized procedural domain knowledge, can generate better keyphrases than a general-purpose algorithm (C4.5). Subjective human evaluation of the keyphrases generated by GenEx suggests that about 80\% of the keyphrases are acceptable to human readers. This level of performance should be satisfactory for a wide variety of applications.}, } @article{Wondergem00, author = {B.C.M. Wondergem and P. Van Bommel and Th.P. Van Der Weide}, title = {Matching Index Expressions for Information Retrieval}, journal = {Information Retrieval}, publisher = {Kluwer Academic Publishers}, issn = {1386-4564}, number = {4}, volume = {2}, pages = {337--360}, year = {2000}, url = {http://www.kluweronline.com/issn/1386-4564}, abstract = {The INN system is a dynamic hypertext tool for searching and exploring the WWW. It uses a dynamically built ancillary layer to support easy interaction. This layer features the subexpressions of index expressions that are extracted from rendered documents. Currently, the INN system uses keyword based matching. The effectiveness of the INN system may be increased by using matching functions for index expressions. In the design of such functions, several constraints stemming from the INN must be taken into account. Important constraints are a limited response time and storage space, a focus on discriminating (different notions of) subexpressions for index expressions, and domain independency. With these contextual constraints in mind, several matching functions are designed and both theoretically and practically evaluated.}, } @article{Jones00, author = {Gareth Jones and Tetsuya Sakai and Masahiro Kajiura and Kazuo Sumita}, title = {Incremental Relevance Feedback in {Japanese} Text Retrieval}, journal = {Information Retrieval}, publisher = {Kluwer Academic Publishers}, issn = {1386-4564}, number = {4}, volume = {2}, pages = {361--384}, year = {2000}, url = {http://www.kluweronline.com/issn/1386-4564}, abstract = {The application of relevance feedback techniques has been shown to improve retrieval performance for a number of information retrieval tasks. This paper explores incremental relevance feedback for ad hoc Japanese text retrieval; examining, separately and in combination, the utility of term reweighting and query expansion using a probabilistic retrieval model. Retrieval performance is evaluated in terms of standard precision-recall measures, and also using ``number-to-view'' graphs. Experimental results, on the standard BMIR-J2 Japanese language retrieval collection, show that both term reweighting and query expansion improve retrieval performance. This is reflected in improvements in both precision and recall, but also a reduction in the average number of documents which must be viewed to find a selected number of relevant items. In particular, using a simple simulation of user searching, incremental application of relevance information is shown to lead to progressively improved retrieval performance and an overall reduction in the number of documents that a user must view to find relevant ones.}, } @article{Zobel00, author = {Justin Zobel}, title = {Guest Introduction}, journal = {Information Retrieval}, publisher = {Kluwer Academic Publishers}, issn = {1386-4564}, number = {1}, volume = {3}, pages = {5--6}, year = {2000}, url = {http://www.kluweronline.com/issn/1386-4564}, abstract = {}, } @article{Klein00, author = {Shmuel T. Klein}, title = {Skeleton Trees for the Efficient Decoding of {Huffman} Encoded Texts}, journal = {Information Retrieval}, publisher = {Kluwer Academic Publishers}, issn = {1386-4564}, number = {1}, volume = {3}, pages = {7--23}, year = {2000}, url = {http://www.kluweronline.com/issn/1386-4564}, abstract = {A new data structure is investigated, which allows fast decoding of texts encoded by canonical Huffman codes. The storage requirements are much lower than for conventional Huffman trees, $O(log^2 n)$ for trees of depth $O(log n)$, and decoding is faster, because a part of the bit-comparisons necessary for the decoding may be saved. Empirical results on large real-life distributions show a reduction of up to 50\% and more in the number of bit operations. The basic idea is then generalized, yielding further savings.}, } @article{Moffat00, author = {Alistair Moffat and Lang Stuiver}, title = {Binary Interpolative Coding for Effective Index Compression}, journal = {Information Retrieval}, publisher = {Kluwer Academic Publishers}, issn = {1386-4564}, number = {1}, volume = {3}, pages = {25--47}, year = {2000}, url = {http://www.kluweronline.com/issn/1386-4564}, abstract = {Information retrieval systems contain large volumes of text, and currently have typical sizes into the gigabyte range. Inverted indexes are one important method for providing search facilities into these collections, but unless compressed require a great deal of space. In this paper we introduce a new method for compressing inverted indexes that yields excellent compression, fast decoding, and exploits clusteringÑthe tendency for words to appear relatively frequently in some parts of the collection and infrequently in others. We also describe two other quite separate applications for the same compression method: representing the MTF list positions generated by the Burrows-Wheeler Block Sorting transformation; and transmitting the codebook for semi-static block-based minimum-redundancy coding.}, } @article{Navarro00, author = {Gonzalo Navarro and Edleno Silva De Moura and Marden Neubert and Nivio Ziviani and Ricardo Baeza-Yates}, title = {Adding Compression to Block Addressing Inverted Indexes}, journal = {Information Retrieval}, publisher = {Kluwer Academic Publishers}, issn = {1386-4564}, number = {1}, volume = {3}, pages = {49--77}, year = {2000}, url = {http://www.kluweronline.com/issn/1386-4564}, abstract = {Inverted index compression, block addressing and sequential search on compressed text are three techniques that have been separately developed for efficient, low-overhead text retrieval. Modern text compression techniques can reduce the text to less than 30\% of its size and allow searching it directly and faster than the uncompressed text. Inverted index compression obtains significant reduction of its original size at the same processing speed. Block addressing makes the inverted lists point to text blocks instead of exact positions and pay the reduction in space with some sequential text scanning.In this work we combine the three ideas in a single scheme. We present a compressed inverted file that indexes compressed text and uses block addressing. We consider different techniques to compress the index and study their performance with respect to the block size. We compare the index against three separate techniques for varying block sizes, showing that our index is superior to each isolated approach. For instance, with just 4\% of extra space overhead the index has to scan less than 12\% of the text for exact searches and about 20\% allowing one error in the matches.}, } @article{Wermter00, author = {Stefan Wermter}, title = {Neural Network Agents for Learning Semantic Text Classification}, journal = {Information Retrieval}, publisher = {Kluwer Academic Publishers}, issn = {1386-4564}, number = {2}, volume = {3}, pages = {87--103}, year = {2000}, url = {http://www.kluweronline.com/issn/1386-4564}, abstract = {The research project AgNeT develops Agents for Neural Text routing in the internet. Unrestricted potentially faulty text messages arrive at a certain delivery point (e.g. email address or world wide web address). These text messages are scanned and then distributed to one of several expert agents according to a certain task criterium. Possible specific scenarios within this framework include the learning of the routing of publication titles or news titles. In this paper we describe extensive experiments for semantic text routing based on classified library titles and newswire titles. This task is challenging since incoming messages may contain constructions which have not been anticipated. Therefore, the contributions of this research are in learning and generalizing neural architectures for the robust interpretation of potentially noisy unrestricted messages. Neural networks were developed and examined for this topic since they support robustness and learning in noisy unrestricted real-world texts. We describe and compare different sets of experiments. The first set of experiments tests a recurrent neural network for the task of library title classification. Then we describe a larger more difficult newswire classification task from information retrieval. The comparison of the examined models demonstrates that techniques from information retrieval integrated into recurrent plausibility networks performed well even under noise and for different corpora.}, } @article{Moghrabi00, author = {I.A.R. Moghrabi and R.A. Makholian}, title = {A New Approach to Clustering Records in Information Retrieval Systems}, journal = {Information Retrieval}, publisher = {Kluwer Academic Publishers}, issn = {1386-4564}, number = {2}, volume = {3}, pages = {105--126}, year = {2000}, url = {http://www.kluweronline.com/issn/1386-4564}, abstract = {This work introduces a new approach to record clustering where a hybrid algorithm is presented to cluster records based upon threshold values and the query patterns made to a particular database. The Hamming Distance of a file is used as a measure of space density. The objective of the algorithm is to minimize the Hamming Distance of the file while attaching significance to the most frequent queries being asked. Simulation experiments conducted proved that a great reduction in response time is yielded after the restructuring of a file. We study the space density properties of a file and how it affects retrieval time before and after clustering, as a means of predicting file performance and making appropriate choices of parameters. Criteria, such as, block size, threshold value, percentage of records satisfying a given set of queries, etc., which affect clustering and response time are also studied.}, } @article{McCallum00, author = {Andrew K. McCallum and Kamal Nigam and Jason Rennie and Kristie Seymore}, title = {Automating the Construction of Internet Portals with Machine Learning}, journal = {Information Retrieval}, publisher = {Kluwer Academic Publishers}, issn = {1386-4564}, number = {2}, volume = {3}, pages = {127--163}, year = {2000}, url = {http://www.kluweronline.com/issn/1386-4564}, abstract = {Domain-specific internet portals are growing in popularity because they gather content from the Web and organize it for easy access, retrieval and search. For example, www.campsearch.com allows complex queries by age, location, cost and specialty over summer camps. This functionality is not possible with general, Web-wide search engines. Unfortunately these portals are difficult and time-consuming to maintain. This paper advocates the use of machine learning techniques to greatly automate the creation and maintenance of domain-specific Internet portals. We describe new research in reinforcement learning, information extraction and text classification that enables efficient spidering, the identification of informative text segments, and the population of topic hierarchies. Using these techniques, we have built a demonstration system: a portal for computer science research papers. It already contains over 50,000 papers and is publicly available at www.cora.justresearch.com. These techniques are widely applicable to portal creation in other domains.}, } @article{Schauble00, author = {Peter Sch{\"{a}}uble and Elke Mittendorf}, title = {Introduction}, journal = {Information Retrieval}, publisher = {Kluwer Academic Publishers}, issn = {1386-4564}, number = {3}, volume = {3}, pages = {171--172}, year = {2000}, url = {http://www.kluweronline.com/issn/1386-4564}, abstract = {}, } @article{Wechsler00, author = {Martin Wechsler and Eugen Munteanu and Peter Sch{\"{a}}uble}, title = {New Approaches to Spoken Document Retrieval}, journal = {Information Retrieval}, publisher = {Kluwer Academic Publishers}, issn = {1386-4564}, number = {3}, volume = {3}, pages = {173--188}, year = {2000}, url = {http://www.kluweronline.com/issn/1386-4564}, abstract = {This paper presents four novel techniques for open-vocabulary spoken document retrieval: a method to detect slots that possibly contain a query feature; a method to estimate occurrence probabilities; a technique that we call collection-wide probability re-estimation and a weighting scheme which takes advantage of the fact that long query features are detected more reliably. These four techniques have been evaluated using the TREC-6 spoken document retrieval test collection to determine the improvements in retrieval effectiveness with respect to a baseline retrieval method. Results show that the retrieval effectiveness can be improved considerably despite the large number of speech recognition errors.}, } @article{Mittendorf00, author = {Elke Mittendorf and Peter Sch{\"{a}}uble}, title = {Information Retrieval can Cope with Many Errors}, journal = {Information Retrieval}, publisher = {Kluwer Academic Publishers}, issn = {1386-4564}, number = {3}, volume = {3}, pages = {189--216}, year = {2000}, url = {http://www.kluweronline.com/issn/1386-4564}, abstract = {The retrieval of documents that originate from digitized and OCR-converted paper documents is an important task for modern retrieval systems. The problems that OCR errors cause for the retrieval process have been subject to research for several years now. We approach the problem from a theoretical point of view and model OCR conversion as a random experiment. Our theoretical results, which are supported by experiments, show clearly that information retrieval can cope even with many errors. It is, however, important that the documents are not too short and that recognition errors are distributed appropriately among words and documents. These results disclose that an expensive manual or automatic post-processing of OCR-converted documents usually does not make sense, but that scanning and OCR must be performed in an appropriate way and with care.}, } @article{Wechsler00a, author = {Martin Wechsler and Peter Sch{\"{a}}uble}, title = {The Probability Ranking Principle Revisited}, journal = {Information Retrieval}, publisher = {Kluwer Academic Publishers}, issn = {1386-4564}, number = {3}, volume = {3}, pages = {217--227}, year = {2000}, url = {http://www.kluweronline.com/issn/1386-4564}, abstract = {A theoretic framework for multimedia information retrieval is introduced which guarantees optimal retrieval effectiveness. In particular, a Ranking Principle for Distributed Multimedia-Documents (RPDM) is described together with an algorithm that satisfies this principle. Finally, the RPDM is shown to be a generalization of the Probability Ranking principle (PRP) which guarantees optimal retrieval effectiveness in the case of text document retrieval. The PRP justifies theoretically the relevance ranking adopted by modern search engines. In contrast to the classical PRP, the new RPDM takes into account transmission and inspection time, and most importantly, aspectual recall rather than simple recall.}, } @article{Csillaghy00, author = {A. Csillaghy and H. Hinterberger and A.O. Benz}, title = {Content-Based Image Retrieval in Astronomy}, journal = {Information Retrieval}, publisher = {Kluwer Academic Publishers}, issn = {1386-4564}, number = {3}, volume = {3}, pages = {229--241}, year = {2000}, url = {http://www.kluweronline.com/issn/1386-4564}, abstract = {Content-based image retrieval in astronomy needs methods that can deal with an image content made of noisy and diffuse structures. This motivates investigations on how information should be summarized and indexed for this specific kind of images. The method we present first summarizes the image information content by partitioning the image in regions with same texture. We call this process texture summarization. Second, indexing features are generated by examining the distribution of parameters describing image regions. Indexing features can be associated with global or local image characteristics. Both kinds of indexing features are evaluated on the retrieval system of the Zurich archive of solar radio spectrograms. The evaluation shows that generating local indexing features using self-organizing maps yields the best effectiveness of all tested methods.}, } @article{Mittendorf00a, author = {Elke Mittendorf and Bojidar Mateev and Peter Sch{\"{a}}uble}, title = {Using the Co-occurrence of Words for Retrieval Weighting}, journal = {Information Retrieval}, publisher = {Kluwer Academic Publishers}, issn = {1386-4564}, number = {3}, volume = {3}, pages = {243--251}, year = {2000}, url = {http://www.kluweronline.com/issn/1386-4564}, abstract = {We have applied the well-known Robertson-Sparck Jones weighting to sets of indexing features that are different from word-based features. Our features describe the co-occurrences of words in a window range of predefined size. The experiments have been designed to analyse the value of features that are beyond word-based features but all used retrieval methods can be motivated strictly in the probabilistic framework. Among the several implications of our experiments for weighted retrieval is the surprising result that features that describe the co-occurrences of words in sentence-size or paragraph-size windows are significantly better descriptors than purely word-based indexing features.}, } @article{Baumgarten00, author = {Christoph Baumgarten}, title = {Retrieving Information from a Distributed Heterogeneous Document Collection}, journal = {Information Retrieval}, publisher = {Kluwer Academic Publishers}, issn = {1386-4564}, number = {3}, volume = {3}, pages = {253--271}, year = {2000}, url = {http://www.kluweronline.com/issn/1386-4564}, abstract = {This paper describes a probabilistic model for optimum information retrieval in a distributed heterogeneous environment.The model assumes the collection of documents offered by the environment to be partitioned into subcollections. Documents as well as subcollections have to be indexed, where indexing methods using different indexing vocabularies can be employed. A query provided by a user is answered in terms of a ranked list of documents. The model determines a procedure for ranking the documents that stems from the Probability Ranking Principle: For each subcollection, the subcollection's documents are ranked; the resulting ranked lists are combined into a final ranked list of documents, where the ordering is determined by the documents' probabilities of being relevant with respect to the user's query. Various probabilistic ranking methods may be involved in the distributed ranking process. A criterion for effectively limiting the ranking process to a subset of subcollections extends the model.The property that different ranking methods and indexing vocabularies can be used is important when the subcollections are heterogeneous with respect to their content.The model's applicability is experimentally confirmed. When exploiting the degrees of freedom provided by the model, experiments showed evidence that the model even outperforms comparable models for the non-distributed case with respect to retrieval effectiveness.}, } @article{Braschler00, author = {Martin Braschler and Peter Sch{\"{a}}uble}, title = {Using Corpus-Based Approaches in a System for Multilingual Information Retrieval}, journal = {Information Retrieval}, publisher = {Kluwer Academic Publishers}, issn = {1386-4564}, number = {3}, volume = {3}, pages = {273--284}, year = {2000}, url = {http://www.kluweronline.com/issn/1386-4564}, abstract = {We present a system for multilingual information retrieval that allows users to formulate queries in their preferred language and retrieve relevant information from a collection containing documents in multiple languages. The system is based on a process of document level alignments, where documents of different languages are paired according to their similarity. The resulting mapping allows us to produce a multilingual comparable corpus. Such a corpus has multiple interesting applications. It allows us to build a data structure for query translation in cross-language information retrieval (CLIR). Moreover, we also perform pseudo relevance feedback on the alignments to improve our retrieval results. And finally, multiple retrieval runs can be merged into one unified result list. The resulting system is inexpensive, adaptable to domain-specific collections and new languages and has performed very well at the TREC-7 conference CLIR system comparison.}, } @article{Liddy00, author = {Elizabeth D. Liddy and Ted Diamond and Mary McKenna}, title = {{DR-LINK} in {TIPSTER III}}, journal = {Information Retrieval}, publisher = {Kluwer Academic Publishers}, issn = {1386-4564}, number = {4}, volume = {3}, pages = {291--311}, year = {2000}, url = {http://www.kluweronline.com/issn/1386-4564}, abstract = {A Natural Language Processing based Information Retrieval System that was one of the original systems developed in Phase I of TIPSTER, was the basis of research in TIPSTER III the goal of which was to add two extended capabilities to the core system. Following a description of the multiple levels of linguistic processing that were developed for the original DR-LINK System, details are provided on research into query-specific data fusion and query-specific cross-document summarization. Experimental results show that there is potential for improving retrieval through query-specific fusion and that analysts found the Detailed Multiple Document Summary to be extremely useful for almost every query, while the Thumbnail sketch was useful in approximately 50\% of the queries.}, } @article{Kwok00, author = {K.L. Kwok}, title = {Improving {English} and {Chinese} Ad-Hoc Retrieval: A Tipster Text Phase 3 Project Report}, journal = {Information Retrieval}, publisher = {Kluwer Academic Publishers}, issn = {1386-4564}, number = {4}, volume = {3}, pages = {313--338}, year = {2000}, url = {http://www.kluweronline.com/issn/1386-4564}, abstract = {Both English and Chinese ad-hoc information retrieval were investigated in this Tipster 3 project. Part of our objectives is to study the use of various term level and phrasal level evidence to improve retrieval accuracy. For short queries, we studied five term level techniques that together can lead to good improvements over standard ad-hoc 2-stage retrieval for TREC5-8 experiments. For long queries, we studied the use of linguistic phrases to re-rank retrieval lists. Its effect is small but consistently positive.For Chinese IR, we investigated three simple representations for documents and queries: short-words, bigrams and characters. Both approximate short-word segmentation or bigrams, augmented with characters, give highly effective results. Accurate word segmentation appears not crucial for overall result of a query set. Character indexing by itself is not competitive. Additional improvements may be obtained using collection enrichment and combination of retrieval lists.Our PIRCS document-focused retrieval is also shown to have similarity with a simple language model approach to IR.}, } @article{Davis00, author = {Mark W. Davis and William C. Ogden}, title = {Towards Universal Text Retrieval: Tipster Text Retrieval Research at {New Mexico State University}}, journal = {Information Retrieval}, publisher = {Kluwer Academic Publishers}, issn = {1386-4564}, number = {4}, volume = {3}, pages = {339--356}, year = {2000}, url = {http://www.kluweronline.com/issn/1386-4564}, abstract = {New Mexico State University's Computing Research Lab has participated in research in all three phases of the US Government's Tipster program. Our work on information retrieval has focused on research and development of multilingual and cross-language approaches to automatic retrieval. The work on automatic systems has been supplemented by additional research into the role of the IR system user in interactive retrieval scenarios: monolingual, multilingual and cross-language. The combined efforts suggest that ``universal'' text retrieval, in which a user can find, access and use documents in the face of language differences and information overload, may be possible.}, } @article{Yager00, author = {Ronald R. Yager}, title = {A Hierarchical Document Retrieval Language}, journal = {Information Retrieval}, publisher = {Kluwer Academic Publishers}, issn = {1386-4564}, number = {4}, volume = {3}, pages = {357--377}, year = {2000}, url = {http://www.kluweronline.com/issn/1386-4564}, abstract = {The focus of this work is on the development of a document retrieval language which attempts to enable users to better represent their requirements with respect to retrieved documents. We describe a framework for evaluating documents which allows, in the spirit of computing with words, a linguistic specification of the interrelationship between the desired attributes. This framework, which makes considerable use of the Ordered Weighted Averaging (OWA) operator, also supports a hierarchical structure which allows for an increased expressiveness of queries.}, } @article{Zhang01, author = {Tong Zhang and Frank J. Oles}, title = {Text Categorization Based on Regularized Linear Classification Methods}, journal = {Information Retrieval}, publisher = {Kluwer Academic Publishers}, issn = {1386-4564}, number = {1}, volume = {4}, pages = {5--31}, year = {2001}, url = {http://www.kluweronline.com/issn/1386-4564}, abstract = {A number of linear classification methods such as the linear least squares fit (LLSF), logistic regression, and support vector machines (SVM's) have been applied to text categorization problems. These methods share the similarity by finding hyperplanes that approximately separate a class of document vectors from its complement. However, support vector machines are so far considered special in that they have been demonstrated to achieve the state of the art performance. It is therefore worthwhile to understand whether such good performance is unique to the SVM design, or if it can also be achieved by other linear classification methods. In this paper, we compare a number of known linear classification methods as well as some variants in the framework of regularized linear systems. We will discuss the statistical and numerical properties of these algorithms, with a focus on text categorization. We will also provide some numerical experiments to illustrate these algorithms on a number of datasets.}, } @article{Hawking01, author = {David Hawking and Nick Craswell and Peter Bailey and Kathleen Griffihs}, title = {Measuring Search Engine Quality}, journal = {Information Retrieval}, publisher = {Kluwer Academic Publishers}, issn = {1386-4564}, number = {1}, volume = {4}, pages = {33--59}, year = {2001}, url = {http://www.kluweronline.com/issn/1386-4564}, abstract = {The effectiveness of twenty public search engines is evaluated using TREC-inspired methods and a set of 54 queries taken from real Web search logs. The World Wide Web is taken as the test collection and a combination of crawler and text retrieval system is evaluated. The engines are compared on a range of measures derivable from binary relevance judgments of the first seven live results returned. Statistical testing reveals a significant difference between engines and high intercorrelations between measures. Surprisingly, given the dynamic nature of the Web and the time elapsed, there is also a high correlation between results of this study and a previous study by Gordon and Pathak. For nearly all engines, there is a gradual decline in precision at increasing cutoff after some initial fluctuation. Performance of the engines as a group is found to be inferior to the group of participants in the TREC-8 Large Web task, although the best engines approach the median of those systems. Shortcomings of current Web search evaluation methodology are identified and recommendations are made for future improvements. In particular, the present study and its predecessors deal with queries which are assumed to derive from a need to find a selection of documents relevant to a topic. By contrast, real Web search reflects a range of other information need types which require different judging and different measures.}, } @article{Zhang01a, author = {Jin Zhang}, title = {The Characteristic Analysis of the {DARE} Visual Space}, journal = {Information Retrieval}, publisher = {Kluwer Academic Publishers}, issn = {1386-4564}, number = {1}, volume = {4}, pages = {61--78}, year = {2001}, url = {http://www.kluweronline.com/issn/1386-4564}, abstract = {In this paper a distance-angle-based visual retrieval tool DARE is introduced. The distance-based similarity distribution, angle-based similarity distribution, and the differences of their distributions in the visual space are analyzed. The document cluster analysis in the visual space and the document cluster comparison between the document space and the visual space are addressed. A new conceptÑDistance to Reference AxisÑis introduced to better understand the visual space. The impact of other operations in DARE on the document distribution is discussed. Future research directions including significance of the index term distribution in the visual space and a user study are addressed.}, } @article{Benkhalifa01, author = {Mohammed Benkhalifa and Abdelhak Mouradi and Houssaine Bouyakhf}, title = {Integrating External Knowledge to Supplement Training Data in Semi-Supervised Learning for Text Categorization}, journal = {Information Retrieval}, publisher = {Kluwer Academic Publishers}, issn = {1386-4564}, number = {2}, volume = {4}, pages = {91--113}, year = {2001}, url = {http://www.kluweronline.com/issn/1386-4564}, abstract = {Text Categorization (TC) is the automated assignment of text documents to predefined categories based on document contents. TC has been an application for many learning approaches, which prove effective. Nevertheless, TC provides many challenges to machine learning. In this paper, we suggest, for text categorization, the integration of external WordNet lexical information to supplement training data for a semi-supervised clustering algorithm which can learn from both training and test documents to classify new unseen documents. This algorithm is the ``Semi-Supervised Fuzzy c-Means'' (ssFCM). Our experiments use Reuters 21578 database and consist of binary classifications for categories selected from the 115 TOPICS classes of the Reuters collection. Using the Vector Space Model, each document is represented by its original feature vector augmented with external feature vector generated using WordNet. We verify experimentally that the integration of WordNet helps ssFCM improve its performance, effectively addresses the classification of documents into categories with few training documents and does not interfere with the use of training data.}, } @article{Kim01, author = {Jee-Hyub Kim and Byung-Kwan Kwak and Seungwoo Lee and Geunbae Lee and Jong-Hyeok Lee}, title = {A Corpus-Based Learning Method of Compound Noun Indexing Rules for {Korean}}, journal = {Information Retrieval}, publisher = {Kluwer Academic Publishers}, issn = {1386-4564}, number = {2}, volume = {4}, pages = {115--132}, year = {2001}, url = {http://www.kluweronline.com/issn/1386-4564}, abstract = {In Korean information retrieval, compound nouns play an important role in improving precision in search experiments. There are two major approaches to compound noun indexing in Korean: statistical and linguistic. Each method, however, has its own shortcomings, such as limitations when indexing diverse types of compound nouns, over-generation of compound nouns, and data sparseness in training. In this paper, we propose a corpus-based learning method, which can index diverse types of compound nouns using rules automatically extracted from a large corpus. The automatic learning method is more portable and requires less human effort, although it exhibits a performance level similar to the manual-linguistic approach. We also present a new filtering method to solve the problems of compound noun over-generation and data sparseness.}, } @article{Goldberg01, author = {Ken Goldberg and Theresa Roeder and Dhruv Gupta and Chris Perkins}, title = {Eigentaste: A Constant Time Collaborative Filtering Algorithm}, journal = {Information Retrieval}, publisher = {Kluwer Academic Publishers}, issn = {1386-4564}, number = {2}, volume = {4}, pages = {133--151}, year = {2001}, url = {http://www.kluweronline.com/issn/1386-4564}, abstract = {Eigentaste is a collaborative filtering algorithm that uses universal queries to elicit real-valued user ratings on a common set of items and applies principal component analysis (PCA) to the resulting dense subset of the ratings matrix. PCA facilitates dimensionality reduction for offline clustering of users and rapid computation of recommendations. For a database of n users, standard nearest-neighbor techniques require O(n) processing time to compute recommendations, whereas Eigentaste requires O(1) (constant) time. We compare Eigentaste to alternative algorithms using data from Jester, an online joke recommending system.Jester has collected approximately 2,500,000 ratings from 57,000 users. We use the Normalized Mean Absolute Error (NMAE) measure to compare performance of different algorithms. In the Appendix we use Uniform and Normal distribution models to derive analytic estimates of NMAE when predictions are random. On the Jester dataset, Eigentaste computes recommendations two orders of magnitude faster with no loss of accuracy. Jester is online at: http://eigentaste.berkeley.edu}, } @article{Lopresti01, author = {Daniel P. Lopresti}, title = {A Comparison of Text-Based Methods for Detecting Duplication in Scanned Document Databases}, journal = {Information Retrieval}, publisher = {Kluwer Academic Publishers}, issn = {1386-4564}, number = {2}, volume = {4}, pages = {153--173}, year = {2001}, url = {http://www.kluweronline.com/issn/1386-4564}, abstract = {This paper presents an experimental evaluation of several text-based methods for detecting duplication in scanned document databases using uncorrected OCR output. This task is made challenging both by the wide range of degradations printed documents can suffer, and by conflicting interpretations of what it means to be a ``duplicate.'' We report results for four sets of experiments exploring various aspects of the problem space. While the techniques studied are generally robust in the face of most types of OCR errors, there are nonetheless important differences which we identify and discuss in detail.}, } @article{Kekalainen01, author = {Jaana Kek{\"{a}}l{\"{a}}inen}, title = {Information Retrieval Special Issue: Conceptual, Linguistic and Task-Based {IR}: Research at the {University of Tampere}}, journal = {Information Retrieval}, publisher = {Kluwer Academic Publishers}, issn = {1386-4564}, number = {3/4}, volume = {4}, pages = {191--194}, year = {2001}, url = {http://www.kluweronline.com/issn/1386-4564}, abstract = {}, } @article{Alkula01, author = {Riitta Alkula}, title = {From Plain Character Strings to Meaningful Words: Producing Better Full Text Databases for Inflectional and Compounding Languages with Morphological Analysis Software}, journal = {Information Retrieval}, publisher = {Kluwer Academic Publishers}, issn = {1386-4564}, number = {3/4}, volume = {4}, pages = {195--208}, year = {2001}, url = {http://www.kluweronline.com/issn/1386-4564}, abstract = {The paper deals with linguistic processing and retrieval techniques in fulltext databases. Special attention is focused on the characteristics of highly inflectional languages, and how morphological structure of a language should be taken into account, when designing and developing information retrieval systems. Finnish is used as an example of a language, which has a more complicated inflectional structure than the English language. In the FULLTEXT project, natural language analysis modules for Finnish were incorporated into the commercial BASIS information retrieval system, which is based on inverted files and Boolean searching. Several test databases were produced, each using one or two Finnish morphological analysis programs.}, } @article{Pirkola01, author = {Ari Pirkola and Turid Hedlund and Heikki Keskustalo and Kalervo J{\"{a}}rvelin}, title = {Dictionary-Based Cross-Language Information Retrieval: Problems, Methods, and Research Findings}, journal = {Information Retrieval}, publisher = {Kluwer Academic Publishers}, issn = {1386-4564}, number = {3/4}, volume = {4}, pages = {209--230}, year = {2001}, url = {http://www.kluweronline.com/issn/1386-4564}, abstract = {This paper reviews literature on dictionary-based cross-language information retrieval (CLIR) and presents CLIR research done at the University of Tampere (UTA). The main problems associated with dictionary-based CLIR, as well as appropriate methods to deal with the problems are discussed. We will present the structured query model by Pirkola and report findings for four different language pairs concerning the effectiveness of query structuring. The architecture of our automatic query translation and construction system is presented.}, } @article{Jarvelinen01, author = {Kalervo J{\"{a}}rvelin and Jaana Kek{\"{a}}l{\"{a}}inen and Timo Niemi}, title = {{ExpansionTool}: Concept-Based Query Expansion and Construction}, journal = {Information Retrieval}, publisher = {Kluwer Academic Publishers}, issn = {1386-4564}, number = {3/4}, volume = {4}, pages = {231--255}, year = {2001}, url = {http://www.kluweronline.com/issn/1386-4564}, abstract = {We develop a deductive data model for concept-based query expansion. It is based on three abstraction levels: the conceptual, linguistic and string levels. Concepts and relationships among them are represented at the conceptual level. The linguistic level gives natural language expressions for concepts. Each expression has one or more matching patterns at the string level. The models specify the matching of the expression in database indices built in varying ways. The data model supports a declarative concept-based query expansion and formulation tool, the ExpansionTool, for heterogeneous IR system environments. Conceptual expansion is implemented by a novel intelligent operator for traversing transitive relationships among cyclic concept networks. The number of expansion links followed, their types, and weights can be used to control expansion. A sample empirical experiment illustrating the use of the ExpansionTool in IR experiments is presented.}, } @article{Sormunen01, author = {Eero Sormunen}, title = {Extensions to the {STAIRS} Study -- Empirical Evidence for the Hypothesised Ineffectiveness of {Boolean} Queries in Large Full-Text Databases}, journal = {Information Retrieval}, publisher = {Kluwer Academic Publishers}, issn = {1386-4564}, number = {3/4}, volume = {4}, pages = {257--273}, year = {2001}, url = {http://www.kluweronline.com/issn/1386-4564}, abstract = {The STAIRS study conducted by Blair and Maron in the mid-80's is a milestone in the history of IR evaluation. Blair and Maron made strong conclusion about the inadequacy of free-text searching large databases, and their study has been widely referred in the literature to justify the problems of effectiveness in IR systems. However, some critics of the study have plausibly pointed out that the ineffectiveness conclusions were not solidly based on empirical data.This paper introduces a new theoretical and empirical approach to study the problems of high recall searching in large databases and reports the results of a case experiment. The findings verify some of the hypothetical conclusions introduced in the STAIRS study, and expands the picture of falling performance. It is shown that low precision in high recall searching is unavoidable in exact-match Boolean searching since even major concepts are often expressed implicitly in relevant documents. The author suggests that the problem could be reduced in facet-based best-match searching.}, } @article{Markkula01, author = {Marjo Markkula and Marius Tico and Bemmu Sepponen and Katja Nirkkonen and Eero Sormunen}, title = {A Test Collection for the Evaluation of Content-Based Image Retrieval Algorithms -- A User and Task-Based Approach}, journal = {Information Retrieval}, publisher = {Kluwer Academic Publishers}, issn = {1386-4564}, number = {3/4}, volume = {4}, pages = {275--293}, year = {2001}, url = {http://www.kluweronline.com/issn/1386-4564}, abstract = {Content-based image retrieval (CBIR) algorithms have been seen as a promising access method for digital photograph collections. Unfortunately, we have very little evidence of the usefulness of these algorithms in real user needs and contexts. In this paper, we introduce a test collection for the evaluation of CBIR algorithms. In the test collection, the performance testing is based on photograph similarity perceived by end-users in the context of realistic illustration tasks and environment. The building process and the characteristics of the resulting test collection are outlined, including a typology of similarity criteria expressed by the subjects judging the similarity of photographs. A small-scale study on the consistency of similarity assessments is presented. A case evaluation of two CBIR algorithms is reported. The results show clear correlation between the subjects' similarity assessments and the functioning of feature parameters of the tested algorithms.}, } @article{Vakkari01, author = {Pertti Vakkari}, title = {Changes in Search Tactics and Relevance Judgements when Preparing a Research Proposal -- A Summary of the Findings of a Longitudinal Study}, journal = {Information Retrieval}, publisher = {Kluwer Academic Publishers}, issn = {1386-4564}, number = {3/4}, volume = {4}, pages = {295--310}, year = {2001}, url = {http://www.kluweronline.com/issn/1386-4564}, abstract = {The article summarizes the empirical results on relations between students' problem stages in the course of writing their research proposals for their master's theses and the information sought, choice of search terms and tactics and relevance assessments of the information found for that task. The study is based on Kuhlthau's model of the information search process. The results of the study show that there is a close connection between the students' problem stages (mental model) in the task performance and the information sought, search tactics used, and the assessment of the relevance and utility of the information found. The corroborated hypotheses extend and specify ideas in Kuhlthau's model in the domain of IR. A theory of task-based information searching based on the empirical findings of the study is presented.}, } @article{Kolcz02, author = {Aleksander Ko{\l}cz and Joshua Alspector}, title = {Asymmetric Missing-data Problems: Overcoming the Lack of Negative Data in Preference Ranking}, journal = {Information Retrieval}, publisher = {Kluwer Academic Publishers}, issn = {1386-4564}, number = {1}, volume = {5}, pages = {5--40}, year = {2002}, url = {http://www.kluweronline.com/issn/1386-4564}, abstract = {In certain classification problems there is a strong a asymmetry between the number of labeled examples available for each of the classes involved. In an extreme case, there may be a complete lack of labeled data for one of the classes while, at the same time, there are adequate labeled examples for the others, accompanied by a large body of unlabeled data. Since most classification algorithms require some information about all classes involved, label estimation for the un-represented class is desired. An important representative of this group of problems is that of user interest/preference modeling where there may be a large number of examples of what the user likes with essentially no counterexamples.Recently, there has been much interest in applying the EM algorithm to incomplete data problems in the area of text retrieval and categorization. We adapt this approach to the asymmetric case of modeling user interests in news articles, where only labeled positive training data are available, with access to a large corpus of unlabeled documents. User modeling is here equivalent to that of user-specific document ranking. EM is used in conjunction with the Naive Bayes model while its output is also utilized by a Support Vector Machine and RocchioÕs technique.Our findings demonstrate that the EM algorithm can be quite effective in modeling the negative class under a number of different initialization schemes. Although primarily just the negative training examples are needed, a natural question is whether using all of the estimated labels (i.e., positive and negative) would be more (or less) beneficial. This is important considering that, in this context, the initialization of the negative class for EM is likely not to be very accurate. Experimental results suggest that EM output should be limited to negative label estimates only.}, } @article{Kuriyama02, author = {Kazuko Kuriyama and Noriko Kando and Toshihiko Nozue and Koji Eguchi}, title = {Pooling for a Large-Scale Test Collection: An Analysis of the Search Results from the First {NTCIR} Workshop}, journal = {Information Retrieval}, publisher = {Kluwer Academic Publishers}, issn = {1386-4564}, number = {1}, volume = {5}, pages = {41--59}, year = {2002}, url = {http://www.kluweronline.com/issn/1386-4564}, abstract = {We have conducted a study to: (1) verify the exhaustiveness of pooling for the purpose of constructing a large-scale test collection, and (2) examine whether a difference in the number of pool documents can affect the relative evaluation of IR systems. We carried out the experiments using search topics, their relevance assessments, and the search results that were submitted for both the pre-test and test of the first NTCIR Workshop.Our results verified the efficiency and the effectiveness of the pooling method, the exhaustiveness of the relevance assessments, and the reliability of the evaluation using the test collection based on the pooling method.}, } @article{Chen02, author = {Zhixiang Chen and Binhai Zhu}, title = {Some Formal Analysis of {R}occhioÕs Similarity-Based Relevance Feedback Algorithm}, journal = {Information Retrieval}, publisher = {Kluwer Academic Publishers}, issn = {1386-4564}, number = {1}, volume = {5}, pages = {61--86}, year = {2002}, url = {http://www.kluweronline.com/issn/1386-4564}, abstract = {RocchioÕs similarity-based Relevance feedback algorithm, one of the most important query reformation methods in information retrieval, is essentially an adaptive supervised learning algorithm from examples. In spite of its popularity in various applications there is little rigorous analysis of its learning complexity in literature. In this paper we show that in the binary vector space model, if the initial query vector is 0, then for any of the four typical similarities (inner product, dice coefficient, cosine coefficient, and Jaccard coefficient), RocchioÕs similarity-based relevance feedback algorithm makes at least n mistakes when used to search for a collection of documents represented by a monotone disjunction of at most k relevant features (or terms) over the n-dimensional binary vector space ${0, 1}^n$. When an arbitrary initial query vector in {0, 1}^n is used, it makes at least (n + k - 3)/2 mistakes to search for the same collection of documents. The linear lower bounds are independent of the choices of the threshold and coefficients that the algorithm may use in updating its query vector and making its classification.}, } @article{Ruiz02, author = {Miguel E. Ruiz and Padmini Srinivasan}, title = {Hierarchical Text Categorization Using Neural Networks}, journal = {Information Retrieval}, publisher = {Kluwer Academic Publishers}, issn = {1386-4564}, number = {1}, volume = {5}, pages = {87--118}, year = {2002}, url = {http://www.kluweronline.com/issn/1386-4564}, abstract = {This paper presents the design and evaluation of a text categorization method based on the Hierarchical Mixture of Experts model. This model uses a divide and conquer principle to define smaller categorization problems based on a predefined hierarchical structure. The final classifier is a hierarchical array of neural networks. The method is evaluated using the UMLS Metathesaurus as the underlying hierarchical structure, and the OHSUMED test set of MEDLINE records. Comparisons with an optimized version of the traditional RocchioÕs algorithm adapted for text categorization, as well as flat neural network classifiers are provided. The results show that the use of the hierarchical structure improves text categorization performance with respect to an equivalent flat model. The optimized Rocchio algorithm achieves a performance comparable with that of the hierarchical neural networks.}, } @article{Robertson02, author = {Stephen Robertson}, title = {Introduction to the Special Issue: Overview of the TREC Routing and Filtering Tasks}, journal = {Information Retrieval}, publisher = {Kluwer Academic Publishers}, issn = {1386-4564}, number = {2--3}, volume = {5}, pages = {127--137}, year = {2002}, url = {http://www.kluweronline.com/issn/1386-4564}, abstract = {This paper introduces the special issue, and reviews the routing and filtering tasks as defined and evaluated at TREC. The tasks attempt to simulate a specific service situation: the system is assumed to process an incoming stream of documents against profiles of user interest, strictly in the time order in which they arrive, and immediately refer any matching document to the user. In the adaptive filtering version of the task, the user is assumed to provide a relevance judgement instantly. The rationale for the task definitions and the evaluation measures used is discussed.}, } @article{Allan02, author = {James Allan}, title = {Detection As Multi-Topic Tracking}, journal = {Information Retrieval}, publisher = {Kluwer Academic Publishers}, issn = {1386-4564}, number = {2--3}, volume = {5}, pages = {139--157}, year = {2002}, url = {http://www.kluweronline.com/issn/1386-4564}, abstract = {The topic tracking task from TDT is a variant of information filtering tasks that focuses on event-based topics in streams of broadcast news. In this study, we compare tracking to another TDT task, detection, which has the goal of partitioning all arriving news into topics, regardless of whether the topics are of interest to anyone, and even when a new topic appears that had not been previous anticipated. There are clear relationships between the two tasks (under some assumptions, a ``perfect'' tracking system could ``solve'' the detection problem), but they are evaluated quite differently. We describe the two tasks and discuss their similarities. We show how viewing detection as a form of multi-topic parallel tracking can illuminate the performance tradeoffs of detection over tracking.}, } @article{Ault02, author = {Thomas G. Ault and Yiming Yang}, title = {Information Filtering in TREC-9 and TDT-3: A Comparative Analysis}, journal = {Information Retrieval}, publisher = {Kluwer Academic Publishers}, issn = {1386-4564}, number = {2--3}, volume = {5}, pages = {159--187}, year = {2002}, url = {http://www.kluweronline.com/issn/1386-4564}, abstract = {Much work on automated information filtering has been done in the TREC and TDT domains, but differences in corpora, the nature of TREC topics vs. TDT events, the constraints imposed on training and testing, and the choices of performance measures confound any meaningful comparison between these domains. We attempt to bridge the gap between them by evaluating the performance of the k-nearest-neighbor (kNN) classification system on the corpus and categories from one domain using the constraints of the other. To maximize comparability and understand the effect of the evaluation metrics specific to each domain, we optimize the performance of kNN separately for the F1, T9P (preferred metric for TREC-9) and Ctrk (official metric for TDT-3) metrics. Through a thorough comparison of our within-domain and cross-domain results, our results demonstrate that the corpus used for TREC-9 is more challenging for an information filtering system than the TDT-3 corpus and strongly suggest that the TDT-3 event tracking task itself is more difficult than the TREC batch filtering task. We also show that optimizing performance in TREC-9 and TDT-3 tends to result in systems with different performance characteristics, confounding any meaningful comparison between the two domains, and that T9P and Ctrk both have properties that make them undesirable as general information filtering metrics.}, } @article{Soboroff02, author = {Ian M. Soboroff and Charles K. Nicholas}, title = {Related, but not Relevant: Content-Based Collaborative Filtering in TREC-8}, journal = {Information Retrieval}, publisher = {Kluwer Academic Publishers}, issn = {1386-4564}, number = {2--3}, volume = {5}, pages = {189--208}, year = {2002}, url = {http://www.kluweronline.com/issn/1386-4564}, abstract = {Historically, solutions to the TREC filtering tasks have focused exclusively on the content of documents and search topic descriptions as training data. These approaches are well-known for their ability to focus on those salient concepts in the document stream which are most useful for separating relevant documents from irrelevant ones. However, one kind of information that has not been used is the relationships among the topics themselves. In our TREC-8 routing experiments, we employed a collaborative (or social) filtering algorithm, based on latent semantic indexing which highlights common term usage patterns among groups of filtering profiles. Our hypothesis was that this would allow related topics to share common relevant documents. We found, however, that the algorithm also recommends many documents of related, yet irrelevant interest. As a result of this process, many similar search topics are ``linked'' together by common sets of documents recommended to them. We visualize these topic relationships using graphs where topics are nodes and edges exist where two topics share a recommended document.}, } @article{Eichmann02, author = {David Eichmann and Padmini Srinivasan}, title = {Adaptive Filtering of Newswire Stories using Two-Level Clustering}, journal = {Information Retrieval}, publisher = {Kluwer Academic Publishers}, issn = {1386-4564}, number = {2--3}, volume = {5}, pages = {209--237}, year = {2002}, url = {http://www.kluweronline.com/issn/1386-4564}, abstract = {Adaptive filtering of news is an area of information retrieval gaining substantial interest as services become more available on the Internet. This paper reports on a number of experiments involving a two-level clustering approach using a variety of techniques including threshold adaptation, topic vocabulary adaptation and both noun phrase and named entity adaptation. Our goal in this exploratory research is to empirically compare alternative configurations of our filtering approach that will allow us to better understand the relative value of the component subsystems.}, } @article{Robertson02a, author = {Stephen Robertson}, title = {Threshold Setting and Performance Optimization in Adaptive Filtering}, journal = {Information Retrieval}, publisher = {Kluwer Academic Publishers}, issn = {1386-4564}, number = {2--3}, volume = {5}, pages = {239--256}, year = {2002}, url = {http://www.kluweronline.com/issn/1386-4564}, abstract = {An experimental adaptive filtering system, built on the Okapi search engine, is described. In addition to the regular text retrieval functions, the system requires a complex set of procedures for setting score thresholds and adapting them following feedback. These procedures need to be closely related to the evaluation measures to be used. A mixture of quantitative methods relating a threshold to the number of documents expected to be retrieved in a time period, and qualitative methods relating to the probability of relevance, is defined. Experiments under the TREC-9 Adaptive Filtering Track rules are reported. The system is seen to perform reasonably well in comparison with other systems at TREC. Some of the variables that may affect performance are investigated.}, } @article{Robertson02b, author = {Stephen Robertson}, title = {Comparing the Performance of Adaptive Filtering and Ranked Output Systems}, journal = {Information Retrieval}, publisher = {Kluwer Academic Publishers}, issn = {1386-4564}, number = {2--3}, volume = {5}, pages = {257--268}, year = {2002}, url = {http://www.kluweronline.com/issn/1386-4564}, abstract = {Some insight into the behavior of adaptive filtering systems may be gained by comparing them with similar ranked-output retrieval systems. This is not easy; however, a new optimization measure, introduced for the TREC-9 filtering track, makes some such comparison possible. A series of experiments using the TREC-9 filtering data shows that filtering effectiveness is comparable to routing effectiveness, and demonstrates the gains to be made from adaptation.}, } @article{Herlocker02, author = {Jon Herlocker and Joseph A. Konstan and John Riedl}, title = {An Empirical Analysis of Design Choices in Neighborhood-Based Collaborative Filtering Algorithms}, journal = {Information Retrieval}, publisher = {Kluwer Academic Publishers}, issn = {1386-4564}, number = {4}, volume = {5}, pages = {287--310}, year = {2002}, url = {http://www.kluweronline.com/issn/1386-4564}, abstract = {Collaborative filtering systems predict a user's interest in new items based on the recommendations of other people with similar interests. Instead of performing content indexing or content analysis, collaborative filtering systems rely entirely on interest ratings from members of a participating community. Since predictions are based on human ratings, collaborative filtering systems have the potential to provide filtering based on complex attributes, such as quality, taste, or aesthetics. Many implementations of collaborative filtering apply some variation of the neighborhood-based prediction algorithm. Many variations of similarity metrics, weighting approaches, combination measures, and rating normalization have appeared in each implementation. For these parameters and others, there is no consensus as to which choice of technique is most appropriate for what situations, nor how significant an effect on accuracy each parameter has. Consequently, every person implementing a collaborative filtering system must make hard design choices with little guidance. This article provides a set of recommendations to guide design of neighborhood-based prediction systems, based on the results of an empirical study. We apply an analysis framework that divides the neighborhood-based prediction approach into three components and then examines variants of the key parameters in each component. The three components identified are similarity computation, neighbor selection, and rating combination.}, } @article{Nilsson02, author = {Hierarchical Clustering Using Non-Greedy Principal Direction Divisive Partitioning}, title = {Martin Nilsson}, journal = {Information Retrieval}, publisher = {Kluwer Academic Publishers}, issn = {1386-4564}, number = {4}, volume = {5}, pages = {311--321}, year = {2002}, url = {http://www.kluweronline.com/issn/1386-4564}, abstract = {We present a non-greedy version of the recently published Principal Direction Divisive Partitioning (PDDP) algorithm. The PDDP algorithm creates a hierarchical taxonomy of a data set by successively splitting the data into sub-clusters. At each level the cluster with largest variance is split by a hyper-plane orthogonal to its leading principal component. The PDDP algorithm is known to produce high quality clusters, especially when applied to high dimensional data, such as document-word feature matrices. It also scales well with both the size and the dimensionality of the data set. However, at each level only the locally optimal choice of spitting is considered. At a later stage this often leads to a non-optimal global partitioning of the data. The non-greedy version of the PDDP algorithm (NGPDDP) presented in this paper address this problem. At each level multiple alternative splitting strategies are considered. Results from applying the algorithm to generated and real data (feature vectors from sets of text documents) are presented. The results show substantial improvements in the cluster quality.}, } @article{French02, author = {James C. French and Allison L. Powell and Fredric Gey and Natalia Perelman}, title = {Exploiting Manual Indexing to Improve Collection Selection and Retrieval Effectiveness}, journal = {Information Retrieval}, publisher = {Kluwer Academic Publishers}, issn = {1386-4564}, number = {4}, volume = {5}, pages = {323--351}, year = {2002}, url = {http://www.kluweronline.com/issn/1386-4564}, abstract = {Vocabulary incompatibilities arise when the terms used to index a document collection are largely unknown, or at least not well-known to the users who eventually search the collection. No matter how comprehensive or well-structured the indexing vocabulary, it is of little use if it is not used effectively in query formulation. This paper demonstrates that techniques for mapping user queries into the controlled indexing vocabulary have the potential to radically improve document retrieval performance. We also show how the use of controlled indexing vocabulary can be employed to achieve performance gains for collection selection. Finally, we demonstrate the potential benefit of combining these two techniques in an interactive retrieval environment. Given a user query, our evaluation approach simulates the human user's choice of terms for query augmentation given a list of controlled vocabulary terms suggested by a system. This strategy lets us evaluate interactive strategies without the need for human subjects.}, } @article{Bookstein02, author = {Abraham Bookstein and Vladimir A. Kulyukin and Timo Raita}, title = {Generalized {H}amming Distance}, journal = {Information Retrieval}, publisher = {Kluwer Academic Publishers}, issn = {1386-4564}, number = {4}, volume = {5}, pages = {353--375}, year = {2002}, url = {http://www.kluweronline.com/issn/1386-4564}, abstract = {Many problems in information retrieval and related fields depend on a reliable measure of the distance or similarity between objects that, most frequently, are represented as vectors. This paper considers vectors of bits. Such data structures implement entities as diverse as bitmaps that indicate the occurrences of terms and bitstrings indicating the presence of edges in images. For such applications, a popular distance measure is the Hamming distance. The value of the Hamming distance for information retrieval applications is limited by the fact that it counts only exact matches, whereas in information retrieval, corresponding bits that are close by can still be considered to be almost identical. We define a ``Generalized Hamming distance'' that extends the Hamming concept to give partial credit for near misses, and suggest a dynamic programming algorithm that permits it to be computed efficiently. We envision many uses for such a measure. In this paper we define and prove some basic properties of the ``Generalized Hamming distance'', and illustrate its use in the area of object recognition. We evaluate our implementation in a series of experiments, using autonomous robots to test the measure's effectiveness in relating similar bitstrings.}, } @article{Trotman03, author = {Andrew Trotman}, title = {Compressing Inverted Files}, journal = {Information Retrieval}, publisher = {Kluwer Academic Publishers}, issn = {1386-4564}, number = {1}, volume = {6}, pages = {5--19}, year = {2003}, url = {http://www.kluweronline.com/issn/1386-4564}, abstract = {Research into inverted file compression has focused on compression ratioÑhow small the indexes can be. Compression ratio is important for fast interactive searching. It is taken as read, the smaller the index, the faster the search. The premise ``smaller is better'' may not be true. To truly build faster indexes it is often necessary to forfeit compression. For inverted lists consisting of only 128 occurrences compression may only add overhead. Perhaps the inverted list could be stored in 128 bytes in place of 128 words, but it must still be stored on disk. If the minimum disk sector read size is 512 bytes and the word size is 4 bytes, then both the compressed and raw postings would require one disk seek and one disk sector read. A less efficient compression technique may increase the file size, but decrease load/decompress time, thereby increasing throughput. Examined here are five compression techniques, Golomb, Elias gamma, Elias delta, Variable Byte Encoding and Binary Interpolative Coding. The effect on file size, file seek time, and file read time are all measured as is decompression time. A quantitative measure of throughput is developed and the performance of each method is determined.}, } @article{vanderPol03, author = {Van Der Pol, Ruud}, title = {{Dipe-D}: A Tool for Knowledge-Based Query Formulation in Information Retrieval}, journal = {Information Retrieval}, publisher = {Kluwer Academic Publishers}, issn = {1386-4564}, number = {1}, volume = {6}, pages = {21--47}, year = {2003}, url = {http://www.kluweronline.com/issn/1386-4564}, abstract = {The paper reports the development of Dipe-D, a knowledge-based procedure for the formulation of Boolean queries in information retrieval. Dipe-D creates a query in two steps: (1) the user's information need is developed interactively, while identifying the concepts of the information need, and subsequently (2) the collection of concepts identified is automatically transformed into a Boolean query. In the first step, the subject areaÑas represented in a knowledge baseÑis explored by the user. He does this by means of specifying the (concepts that meet his) information need in an artificial language and looking through the solution as provided by the computer. The specification language allows one to specify concepts by their features, both in precise terms as well as vaguely. By repeating the process of specifying the information need and exploring the resulting concepts, the user may precisely single out the concepts that describe his information need. In the second step, the program provides the designations (and variants) for the concepts identified, and connects them by appropriate operators. Dipe-D is meant to improve on existing procedures that identify the concepts less systematically, create a query manually, and then sometimes expand that query. Experiments are reported on each of the two steps; they indicate that the first step identifies only but not all the relevant concepts, and the second step performs (at least) as good as human beings do.}, } @article{Sakkis03, author = {Georgios Sakkis and Ion Androutsopoulos and Georgios Paliouras and Vangelis Karkaletsis and Constantine D. Spyropoulos and Panagiotis Stamatopoulos}, title = {A Memory-Based Approach to Anti-Spam Filtering for Mailing Lists}, journal = {Information Retrieval}, publisher = {Kluwer Academic Publishers}, issn = {1386-4564}, number = {1}, volume = {6}, pages = {49--73}, year = {2003}, url = {http://www.kluweronline.com/issn/1386-4564}, abstract = {This paper presents an extensive empirical evaluation of memory-based learning in the context of anti-spam filtering, a novel cost-sensitive application of text categorization that attempts to identify automatically unsolicited commercial messages that flood mailboxes. Focusing on anti-spam filtering for mailing lists, a thorough investigation of the effectiveness of a memory-based anti-spam filter is performed using a publicly available corpus. The investigation includes different attribute and distance-weighting schemes, and studies on the effect of the neighborhood size, the size of the attribute set, and the size of the training corpus. Three different cost scenarios are identified, and suitable cost-sensitive evaluation functions are employed. We conclude that memory-based anti-spam filtering for mailing lists is practically feasible, especially when combined with additional safety nets. Compared to a previously tested Naive Bayes filter, the memory-based filter performs on average better, particularly when the misclassification cost for non-spam messages is high.}, } @article{Elovici03, author = {Yuval Elovici and Bracha Shapira and Paul B. Kantor}, title = {Using the Information Structure Model to Compare Profile-Based Information Filtering Systems}, journal = {Information Retrieval}, publisher = {Kluwer Academic Publishers}, issn = {1386-4564}, number = {1}, volume = {6}, pages = {75--97}, year = {2003}, url = {http://www.kluweronline.com/issn/1386-4564}, abstract = {In the IR field it is clear that the value of a system depends on the cost and benefit profiles of its users. It would seem obvious that different users would prefer different systems. In the TREC-9 filtering track, systems are evaluated by a utility measure specifying a given cost and benefit. However, in the study of decision systems it is known that, in some cases, one system may be unconditionally better than another. In this paper we employ a decision theoretic approach to find conditions under which an Information Filtering (IF) system is unconditionally superior to another for all users regardless of their cost and benefit profiles. It is well known that if two IF systems have equal precision the system with better recall will be preferred by all users. Similarly, with equal recall, better precision is universally preferred. We confirm these known results and discover an unexpected dominance relation in which a system with lower recall will be universally preferred provided its precision is sufficiently higher.}, } @article{Hawking03, author = {David Hawking and Stephen Robertson}, title = {On Collection Size and Retrieval Effectiveness}, journal = {Information Retrieval}, publisher = {Kluwer Academic Publishers}, issn = {1386-4564}, number = {1}, volume = {6}, pages = {99--105}, year = {2003}, url = {http://www.kluweronline.com/issn/1386-4564}, abstract = {The relationship between collection size and retrieval effectiveness is particularly important in the context of Web search. We investigate it first analytically and then experimentally, using samples and subsets of test collections. Different retrieval systems vary in how the score assigned to an individual document in a sample collection relates to the score it receives in the full collection; we identify four cases. We apply signal detection (SD) theory to retrieval from samples, taking into account the four cases and using a variety of shapes for relevant and irrelevant distributions. We note that the SD model subsumes several earlier hypotheses about the causes of the decreased precision in samples. We also discuss other models which contribute to an understanding of the phenomenon, particularly relating to the effects of discreteness. Different models provide complementary insights. Extensive use is made of test data, some from official submissions to the TREC-6 VLC track and some new, to illustrate the effects and test hypotheses. We empirically confirm predictions, based on SD theory, that P@n should decline when moving to a sample collection and that average precision and R-precision should remain constant. SD theory suggests the use of recall-fallout plots as operating characteristic (OC) curves. We plot OC curves of this type for a real retrieval system and query set and show that curves for sample collections are similar but not identical to the curve for the full collection.}, } @article{Lu03, author = {Zhihong Lu and Kathryn S. McKinley}, title = {Partial Collection Replication for Information Retrieval}, journal = {Information Retrieval}, publisher = {Kluwer Academic Publishers}, issn = {1386-4564}, number = {2}, volume = {6}, pages = {159--198}, year = {2003}, url = {http://www.kluweronline.com/issn/1386-4564}, abstract = {The explosion of content in distributed information retrieval (IR) systems requires new mechanisms in order to attain timely and accurate retrieval of unstructured text. This paper shows how to exploit locality by building, using, and searching partial replicas of text collections in a distributed IR system. In this work, a partial replica includes a subset of the documents from larger collection(s) and the corresponding inference network search mechanism. For each query, the distributed system determines if partial replica is a good match and then searches it, or it searches the original collection. We demonstrate the scenarios where partial replication performs better than systems that use caches which only store previous query and answer pairs. We first use logs from THOMAS and Excite to examine query locality using query similarity versus exact match. We show that searching replicas can improve locality (from 3 to 19\%) over the exact match required by caching. Replicas increase locality because they satisfy queries which are distinct but return the same or very similar answers. We then present a novel inference network replica selection function. We vary its parameters and compare it to previous collection selection functions, demonstrating a configuration that directs most of the appropriate queries to replicas in a replica hierarchy. We then explore the performance of partial replication in a distributed IR system. We compare it with caching and partitioning. Our validated simulator shows that the increases in locality due to replication make it preferable to caching alone, and that even a small increase of 4\% in locality translates into a performance advantage. We also show a hybrid system with caches and replicas that performs better than each on their own.}, } @article{Mostafa03, author = {Javed Mostafa and Snehasis Mukhopadhyay and Mathew Palakal}, title = {Simulation Studies of Different Dimensions of Users' Interests and their Impact on User Modeling and Information Filtering}, journal = {Information Retrieval}, publisher = {Kluwer Academic Publishers}, issn = {1386-4564}, number = {2}, volume = {6}, pages = {199--223}, year = {2003}, url = {http://www.kluweronline.com/issn/1386-4564}, abstract = {Modeling users in information filtering systems is a difficult challenge due to dimensions such as nature, scope, and variability of interests. Numerous machine-learning approaches have been proposed for user modeling in filtering systems. The focus has been primarily on techniques for user model capture and representation, with relatively simple assumptions made about the type of users' interests. Although many studies claim to deal with ``adaptive'' techniques and thus they pay heed to the fact that different types of interests must be modeled or even changes in interests have to be captured, few studies have actually focused on the dynamic nature and the variability of user-interests and their impact on the modeling process. A simulation based information filtering environment called SIMSFITER was developed to overcome some of the barriers associated with conducting studies on user-oriented factors that can impact interests. SIMSIFTER implemented a user modeling approach known as reinforcement learning that has proven to be effective in previous filtering studies involving humans. This paper reports on several studies conducted using SIMSIFTER that examined the impact of key dimensions such as type of interests, rate of change of interests and level of user-involvement on modeling accuracy and ultimately on filtering effectiveness.}, } @article{Guo03, author = {David Guo and Michael W. Berry and Bryan B. Thompson and Sidney Bailin}, title = {Knowledge-Enhanced Latent Semantic Indexing}, journal = {Information Retrieval}, publisher = {Kluwer Academic Publishers}, issn = {1386-4564}, number = {2}, volume = {6}, pages = {225--250}, year = {2003}, url = {http://www.kluweronline.com/issn/1386-4564}, abstract = {Latent Semantic Indexing (LSI) is a popular information retrieval model for concept-based searching. As with many vector space IR models, LSI requires an existing term-document association structure such as a term-by-document matrix. The term-by-document matrix, constructed during document parsing, can only capture weighted vocabulary occurrence patterns in the documents. However, for many knowledge domains there are pre-existing semantic structures that could be used to organize and categorize information. The goals of this study are (i) to demonstrate how such semantic structures can be automatically incorporated into the LSI vector space model, and (ii) to measure the effect of these structures on query matching performance. The new approach, referred to as Knowledge-Enhanced LSI, is applied to documents in the OHSUMED medical abstracts collection using the semantic structures provided by the UMLS Semantic Network and MeSH. Results based on precision-recall data (11-point average precision values) indicate that a MeSH-enhanced search index is capable of delivering noticeable incremental performance gain (as much as 35\%) over the original LSI for modest constraints on precision. This performance gain is achieved by replacing the original query with the MeSH heading extracted from the query text via regular expression matches.}, } @article{Vechtomova03, author = {Olga Vechtomova and Stephen Robertson and Susan Jones}, title = {Knowledge-Enhanced Latent Semantic Indexing}, journal = {Information Retrieval}, publisher = {Kluwer Academic Publishers}, issn = {1386-4564}, number = {2}, volume = {6}, pages = {251--273}, year = {2003}, url = {http://www.kluweronline.com/issn/1386-4564}, abstract = {The paper presents two novel approaches to query expansion with long-span collocatesÑwords, significantly co-occurring in topic-size windows with query terms. In the first approachÑglobal collocation analysisÑcollocates of query terms are extracted from the entire collection, in the secondÑlocal collocation analysisÑfrom a subset of retrieved documents. The significance of association between collocates was estimated using modified Mutual Information and Z score. The techniques were tested using the Okapi IR system. The effect of different parameters on performance was evaluated: window size, number of expansion terms, measures of collocation significance and types of expansion terms. We present performance results of these techniques and provide comparison with related approaches.}, } @article{Wang03, author = {Quan Wang and Yiu-Kai Ng}, title = {An Ontology-Based Binary-Categorization Approach for Recognizing Multiple-Record Web Documents Using a Probabilistic Retrieval Model}, journal = {Information Retrieval}, publisher = {Kluwer Academic Publishers}, issn = {1386-4564}, number = {3/4}, volume = {6}, pages = {295--332}, year = {2003}, url = {http://www.kluweronline.com/issn/1386-4564}, abstract = {The Web contains a tremendous amount of information. It is challenging to determine which Web documents are relevant to a user query, and even more challenging to rank them according to their degrees of relevance. In this paper, we propose a probabilistic retrieval model using logistic regression for recognizing multiple-record Web documents against an application ontology, a simple conceptual modeling approach. We notice that many Web documents contain a sequence of chunks of textual information, each of which constitutes a ``record.'' This type of documents is referred to as multiple-record documents. In our categorization approach, a document is represented by a set of term frequencies of index terms, a density heuristic value, and a grouping heuristic value. We first apply the logistic regression analysis on relevant probabilities using the (i) index terms, (ii) density value, and (iii) grouping value of each training document. Hereafter, the relevant probability of each test document is interpolated from the fitting curves. Contrary to other probabilistic retrieval models, our model makes only a weak independent assumption and is capable of handling any important dependent relationships among index terms. In addition, we use logistic regression, instead of linear regression analysis, because the relevance probabilities of training documents are discrete. Using a test set of car-ads and another one for obituary Web documents, our probabilistic model achieves the averaged recall ratio of 100\%, precision ratio of 83.3\%, and accuracy ratio of 92.5\%.}, } @article{Huang03, author = {Xiangji Huang and Fuchun Peng and Dale Schuurmans and Nick Cercone and Stephen E. Robertson}, title = {Applying Machine Learning to Text Segmentation for Information Retrieval}, journal = {Information Retrieval}, publisher = {Kluwer Academic Publishers}, issn = {1386-4564}, number = {3/4}, volume = {6}, pages = {333--362}, year = {2003}, url = {http://www.kluweronline.com/issn/1386-4564}, abstract = {We propose a self-supervised word segmentation technique for text segmentation in Chinese information retrieval. This method combines the advantages of traditional dictionary based, character based and mutual information based approaches, while overcoming many of their shortcomings. Experiments on TREC data show this method is promising. Our method is completely language independent and unsupervised, which provides a promising avenue for constructing accurate multi-lingual or cross-lingual information retrieval systems that are flexible and adaptive. We find that although the segmentation accuracy of self-supervised segmentation is not as high as some other segmentation methods, it is enough to give good retrieval performance. It is commonly believed that word segmentation accuracy is monotonically related to retrieval performance in Chinese information retrieval. However, for Chinese, we find that the relationship between segmentation and retrieval performance is in fact nonmonotonic; that is, at around 70\% word segmentation accuracy an over-segmentation phenomenon begins to occur which leads to a reduction in information retrieval performance. We demonstrate this effect by presenting an empirical investigation of information retrieval on Chinese TREC data, using a wide variety of word segmentation algorithms with word segmentation accuracies ranging from 44\% to 95\%, including 70\% word segmentation accuracy from our self-supervised word-segmentation approach. It appears that the main reason for the drop in retrieval performance is that correct compounds and collocations are preserved by accurate segmenters, while they are broken up by less accurate (but reasonable) segmenters, to a surprising advantage. This suggests that words themselves might be too broad a notion to conveniently capture the general semantic meaning of Chinese text. Our research suggests machine learning techniques can play an important role in building adaptable information retrieval systems and different evaluation standards for word segmentation should be given to different applications.}, } @article{Nottelmann03, author = {Henrik Nottelmann and Norbert Fuhr}, title = {From Retrieval Status Values to Probabilities of Relevance for Advanced IR Applications}, journal = {Information Retrieval}, publisher = {Kluwer Academic Publishers}, issn = {1386-4564}, number = {3/4}, volume = {6}, pages = {363--388}, year = {2003}, url = {http://www.kluweronline.com/issn/1386-4564}, abstract = {Information Retrieval systems typically sort the result with respect to document retrieval status values (RSV). According to the Probability Ranking Principle, this ranking ensures optimum retrieval quality if the RSVs are monotonously increasing with the probabilities of relevance (as e.g. for probabilistic IR models). However, advanced applications like filtering or distributed retrieval require estimates of the actual probability of relevance. The relationship between the RSV of a document and its probability of relevance can be described by a ``normalisation'' function which maps the retrieval status value onto the probability of relevance (``mapping functions''). In this paper, we explore the use of linear and logistic mapping functions for different retrieval methods. In a series of upper-bound experiments, we compare the approximation quality of the different mapping functions. We also investigate the effect on the resulting retrieval quality in distributed retrieval (only merging, without resource selection). These experiments show that good estimates of the actual probability of relevance can be achieved, and that the logistic model outperforms the linear one. Retrieval quality for distributed retrieval is only slightly improved by using the logistic function.}, } @article{Peters04, author = {Carol Peters and Martin Braschler}, title = {Editorial}, journal = {Information Retrieval}, publisher = {Kluwer Academic Publishers}, issn = {1386-4564}, number = {1/2}, volume = {7}, pages = {5}, year = {2004}, url = {http://www.kluweronline.com/issn/1386-4564}, abstract = {}, } @article{Braschler04, author = {Martin Braschler and Carol Peters}, title = {{Cross-Language Evaluation Forum}: Objectives, Results, Achievements}, journal = {Information Retrieval}, publisher = {Kluwer Academic Publishers}, issn = {1386-4564}, number = {1/2}, volume = {7}, pages = {7--31}, year = {2004}, url = {http://www.kluweronline.com/issn/1386-4564}, abstract = {}, } @article{Hollink04, author = {Vera Hollink and Jaap Kamps and Christof Monz and De Rijke, Maarten}, title = {Monolingual Document Retrieval for {E}uropean Languages}, journal = {Information Retrieval}, publisher = {Kluwer Academic Publishers}, issn = {1386-4564}, number = {1/2}, volume = {7}, pages = {33--52}, year = {2004}, url = {http://www.kluweronline.com/issn/1386-4564}, abstract = {}, } @article{Bertoldi04, author = {Nicola Bertoldi and Marcello Federico}, title = {Statistical Models for Monolingual and Bilingual Information Retrieval}, journal = {Information Retrieval}, publisher = {Kluwer Academic Publishers}, issn = {1386-4564}, number = {1/2}, volume = {7}, pages = {53--72}, year = {2004}, url = {http://www.kluweronline.com/issn/1386-4564}, abstract = {}, } @article{McNamee04, author = {Paul McNamee and James Mayfield}, title = {Character N-Gram Tokenization for {E}uropean Language Text Retrieval}, journal = {Information Retrieval}, publisher = {Kluwer Academic Publishers}, issn = {1386-4564}, number = {1/2}, volume = {7}, pages = {73--97}, year = {2004}, url = {http://www.kluweronline.com/issn/1386-4564}, abstract = {}, } @article{Hedlund04, author = {Turid Hedlund and Eija Airio and Heikki Keskustalo and Raija Lehtokangas and Ari Pirkola and Kalervo J{\"{a}}rvelin}, title = {Dictionary-Based Cross-Language Information Retrieval: Learning Experiences from CLEF 2000Ð2002}, journal = {Information Retrieval}, publisher = {Kluwer Academic Publishers}, issn = {1386-4564}, number = {1/2}, volume = {7}, pages = {99--119}, year = {2004}, url = {http://www.kluweronline.com/issn/1386-4564}, abstract = {}, } @article{Savoy04, author = {Jacques Savoy}, title = {Combining Multiple Strategies for Effective Monolingual and Cross-Language Retrieval}, journal = {Information Retrieval}, publisher = {Kluwer Academic Publishers}, issn = {1386-4564}, number = {1/2}, volume = {7}, pages = {121--148}, year = {2004}, url = {http://www.kluweronline.com/issn/1386-4564}, abstract = {}, } @article{Chen04, author = {Aitao Chen and Fredric C. Gey}, title = {Multilingual Information Retrieval Using Machine Translation, Relevance Feedback and Decompounding}, journal = {Information Retrieval}, publisher = {Kluwer Academic Publishers}, issn = {1386-4564}, number = {1/2}, volume = {7}, pages = {149--182}, year = {2004}, url = {http://www.kluweronline.com/issn/1386-4564}, abstract = {}, } @article{Braschler04a, author = {Martin Braschler}, title = {Combination Approaches for Multilingual Text Retrieval}, journal = {Information Retrieval}, publisher = {Kluwer Academic Publishers}, issn = {1386-4564}, number = {1/2}, volume = {7}, pages = {183--204}, year = {2004}, url = {http://www.kluweronline.com/issn/1386-4564}, abstract = {}, } @article{Oard04, author = {Douglas W. Oard and Julio Gonzalo and Mark Sanderson and Fernando L{\'{o}}pez-Ostenero and Jianqiang Wang}, title = {Interactive Cross-Language Document Selection}, journal = {Information Retrieval}, publisher = {Kluwer Academic Publishers}, issn = {1386-4564}, number = {1/2}, volume = {7}, pages = {205--228}, year = {2004}, url = {http://www.kluweronline.com/issn/1386-4564}, abstract = {}, } @article{Sebastiani04, author = {Fabrizio Sebastiani}, title = {Introduction: Special Issue on the {25th European Conference on Information Retrieval Research}}, journal = {Information Retrieval}, publisher = {Kluwer Academic Publishers}, issn = {1386-4564}, number = {3/4}, volume = {7}, pages = {235--237}, year = {2004}, url = {http://www.kluweronline.com/issn/1386-4564}, abstract = {}, } @article{Gurrin04, author = {Cathal Gurrin and Alan F. Smeaton}, title = {Replicating {W}eb Structure in Small-Scale Test Collections}, journal = {Information Retrieval}, publisher = {Kluwer Academic Publishers}, issn = {1386-4564}, number = {3/4}, volume = {7}, pages = {239--263}, year = {2004}, url = {http://www.kluweronline.com/issn/1386-4564}, abstract = {}, } @article{Harper04, author = {David J. Harper and Ivan Koychev and Yixing Sun and Iain Pirie}, title = {Within-Document Retrieval: A User-Centred Evaluation of Relevance Profiling}, journal = {Information Retrieval}, publisher = {Kluwer Academic Publishers}, issn = {1386-4564}, number = {3/4}, volume = {7}, pages = {265--290}, year = {2004}, url = {http://www.kluweronline.com/issn/1386-4564}, abstract = {}, } @article{Braschler04b, author = {Martin Braschler and B{\"{a}}rbel Ripplinger}, title = {How Effective is Stemming and Decompounding for {G}erman Text Retrieval?}, journal = {Information Retrieval}, publisher = {Kluwer Academic Publishers}, issn = {1386-4564}, number = {3/4}, volume = {7}, pages = {291--316}, year = {2004}, url = {http://www.kluweronline.com/issn/1386-4564}, abstract = {}, } @article{Peng04, author = {Fuchun Peng and Dale Schuurmans and Shaojun Wang}, title = {Augmenting Naive {B}ayes Classifiers with Statistical Language Models}, journal = {Information Retrieval}, publisher = {Kluwer Academic Publishers}, issn = {1386-4564}, number = {3/4}, volume = {7}, pages = {317--345}, year = {2004}, url = {http://www.kluweronline.com/issn/1386-4564}, abstract = {}, } @article{Makkonen04, author = {Juha Makkonen and Helena Ahonen-Myka and Marko Salmenkivi}, title = {Simple Semantics in Topic Detection and Tracking}, journal = {Information Retrieval}, publisher = {Kluwer Academic Publishers}, issn = {1386-4564}, number = {3/4}, volume = {7}, pages = {347--368}, year = {2004}, url = {http://www.kluweronline.com/issn/1386-4564}, abstract = {}, } @article{Lin04, author = {Raz Lin and Sarit Kraus and Jeffrey Tew}, title = {OSGS--A Personalized Online Store for E-Commerce Environments}, journal = {Information Retrieval}, publisher = {Kluwer Academic Publishers}, issn = {1386-4564}, number = {3/4}, volume = {7}, pages = {369--394}, year = {2004}, url = {http://www.kluweronline.com/issn/1386-4564}, abstract = {}, } @article{Cheung04, author = {Learning User Similarity and Rating Style for Collaborative Recommendation}, title = {Kwok-Wai Cheung and Lily F. Tian}, journal = {Information Retrieval}, publisher = {Kluwer Academic Publishers}, issn = {1386-4564}, number = {3/4}, volume = {7}, pages = {395--410}, year = {2004}, url = {http://www.kluweronline.com/issn/1386-4564}, abstract = {}, } @article{Spinellis05, author = {Diomidis Spinellis}, title = {Index-Based Persistent Document Identifiers}, journal = {Information Retrieval}, publisher = {Kluwer Academic Publishers}, issn = {1386-4564}, number = {1}, volume = {8}, pages = {5--24}, year = {2005}, url = {http://www.kluweronline.com/issn/1386-4564}, abstract = {The infrastructure of a typical search engine can be used to calculate and resolve persistent document identifiers: a string that can uniquely identify and locate a document on the Internet without reference to its original location (URL). Bookmarking a document using such an identifier allows its retrieval even if the document's URL, and, in many cases, its contents change. Web client applications can offer facilities for users to bookmark a page by reference to a search engine and the persistent identifier instead of the original URL. The identifiers are calculated using a global Internet term index; a document's unique identifier consists of a word or word combination that occurs uniquely in the specific document. We use a genetic algorithm to locate a minimal unique document identifier: the shortest word or word combination that will locate the document. We tested our approach by implementing tools for indexing a document collection, calculating the persistent identifiers, performing queries, and distributing the computation and storage load among many computers.}, } @article{Geffet05, author = {Maayan Geffet and Yair Wiseman and Dror Feitelson}, title = {Automatic Alphabet Recognition}, journal = {Information Retrieval}, publisher = {Kluwer Academic Publishers}, issn = {1386-4564}, number = {1}, volume = {8}, pages = {25--40}, year = {2005}, url = {http://www.kluweronline.com/issn/1386-4564}, abstract = {The last step of the Information Retrieval process is to display the found documents to the user. However, some difficulties might occur at that point. English texts are usually written in the ASCII standard. Unlike the English language, many languages have different character sets, and do not have one standard. This plurality of standards causes problems, especially in a web environment, where one may download a document with an unknown standard. This paper suggests a purely automatic way of finding the standard which was used by the document writer based on the statistical letters distribution in the language. We developed a vector-space-based method that creates frequencies vectors for each letter of the language and then matches a new document's vectors to the pre-computed templates. The algorithm was applied on various types of corpora in Hebrew, Russian and English, and provides an efficient solution to the stated problem in most cases.}, } @article{Xu05, author = {Yunjie Xu and Michel Benaroch}, title = {Information Retrieval with a Hybrid Automatic Query Expansion and Data Fusion Procedure}, journal = {Information Retrieval}, publisher = {Kluwer Academic Publishers}, issn = {1386-4564}, number = {1}, volume = {8}, pages = {41--65}, year = {2005}, url = {http://www.kluweronline.com/issn/1386-4564}, abstract = {We propose a hybrid information retrieval (IR) procedure that builds on two well-known IR approaches: data fusion and query expansion via relevance feedback. This IR procedure is designed to exploit the strengths of data fusion and relevance feedback and to avoid some weaknesses of these approaches. We show that our IR procedure is built on postulates that can be justified analytically and empirically. Additionally, we offer an empirical investigation of the procedure, showing that it is superior to relevance feedback on some dimensions and comparable on other dimensions. The empirical investigation also verifies the conditions under which the use of our IR procedure could be beneficial.}, } @article{Bennett05, author = {Paul N. Bennett and Susan T. Dumais and Eric Horvitz}, title = {The Combination of Text Classifiers Using Reliability Indicators}, journal = {Information Retrieval}, publisher = {Kluwer Academic Publishers}, issn = {1386-4564}, number = {1}, volume = {8}, pages = {67--100}, year = {2005}, url = {http://www.kluweronline.com/issn/1386-4564}, abstract = {The intuition that different text classifiers behave in qualitatively different ways has long motivated attempts to build a better metaclassifier via some combination of classifiers. We introduce a probabilistic method for combining classifiers that considers the context-sensitive reliabilities of contributing classifiers. The method harnesses reliability indicatorsÑvariables that provide signals about the performance of classifiers in different situations. We provide background, present procedures for building metaclassifiers that take into consideration both reliability indicators and classifier outputs, and review a set of comparative studies undertaken to evaluate the methodology.}, } @article{Liggett05, author = {Walter Liggett and Chris Buckley}, title = {System Performance and Natural Language Expression of Information Needs}, journal = {Information Retrieval}, publisher = {Kluwer Academic Publishers}, issn = {1386-4564}, number = {1}, volume = {8}, pages = {101--128}, year = {2005}, url = {http://www.kluweronline.com/issn/1386-4564}, abstract = {Consider information retrieval systems that respond to a query (a natural language statement of a topic, an information need) with an ordered list of 1000 documents from the document collection. From the responses to queries that all express the same topic, one can discern how the words associated with a topic result in particular system behavior. From what is discerned from different topics, one can hypothesize abstract topic factors that influence system performance. An example of such a factor is the specificity of the topic's primary key word. This paper shows that statements about the effect of abstract topic factors on system performance can be supported empirically. A combination of statistical methods is applied to system responses from NIST's Text REtrieval Conference. We analyze each topic using a measure of irrelevant-document exclusion computed for each response and a measure of dissimilarity between relevant-document return orders computed for each pair of responses. We formulate topic factors through graphical comparison of measurements for different topics. Finally, we propose for each topic a four-dimensional summarization that we use to select topic comparisons likely to depict topic factors clearly.}, } @article{Lemire05, author = {Daniel Lemire}, title = {Scale and Translation Invariant Collaborative Filtering Systems}, journal = {Information Retrieval}, publisher = {Kluwer Academic Publishers}, issn = {1386-4564}, number = {1}, volume = {8}, pages = {129--150}, year = {2005}, url = {http://www.kluweronline.com/issn/1386-4564}, abstract = {Collaborative filtering systems are prediction algorithms over sparse data sets of user preferences. We modify a wide range of state-of-the-art collaborative filtering systems to make them scale and translation invariant and generally improve their accuracy without increasing their computational cost. Using the EachMovie and the Jester data sets, we show that learning-free constant time scale and translation invariant schemes outperforms other learning-free constant time schemes by at least 3\% and perform as well as expensive memory-based schemes (within 4\%). Over the Jester data set, we show that a scale and translation invariant Eigentaste algorithm outperforms Eigentaste 2.0 by 20\%. These results suggest that scale and translation invariance is a desirable property.}, } @article{Anh05, author = {Inverted Index Compression Using Word-Aligned Binary Codes}, title = {Vo {Ngoc Anh} and Alistair Moffat}, journal = {Information Retrieval}, publisher = {Kluwer Academic Publishers}, issn = {1386-4564}, number = {1}, volume = {8}, pages = {151--166}, year = {2005}, url = {http://www.kluweronline.com/issn/1386-4564}, abstract = {We examine index representation techniques for document-based inverted files, and present a mechanism for compressing them using word-aligned binary codes. The new approach allows extremely fast decoding of inverted lists during query processing, while providing compression rates better than other high-throughput representations. Results are given for several large text collections in support of these claims, both for compression effectiveness and query efficiency.}, } @article{05, author = {}, title = {}, journal = {Information Retrieval}, publisher = {Kluwer Academic Publishers}, issn = {1386-4564}, number = {2}, volume = {8}, pages = {}, year = {2005}, url = {http://www.kluweronline.com/issn/1386-4564}, abstract = {}, } @article{05, author = {}, title = {}, journal = {Information Retrieval}, publisher = {Kluwer Academic Publishers}, issn = {1386-4564}, number = {3}, volume = {8}, pages = {}, year = {2005}, url = {http://www.kluweronline.com/issn/1386-4564}, abstract = {}, } @article{05, author = {}, title = {}, journal = {Information Retrieval}, publisher = {Kluwer Academic Publishers}, issn = {1386-4564}, number = {4}, volume = {8}, pages = {}, year = {2005}, url = {http://www.kluweronline.com/issn/1386-4564}, abstract = {}, }