text:
791-006-0882-4},
doi = {10.1007/s10791-006-0882-4},
abstract = {We consider text retrieval applications that assign query-specific relevance scores to documents drawn from particular collections. Such applications represent a primary focus of the annual Text Retrieval Conference (TREC), where the participants compare the empirical performance of different approaches.  P   (  K ) , the proportion of the top  K  documents that are relevant, is a popular measure of retrieval effectiveness.Participants in the TREC Very Large Corpus track have observed that when the target is a random sample from a collection,  P   (  K )  is substantially smaller than when the target is the entire collection. Hawking and Robertson (2003) confirmed this finding in a number of experimental settings. Hawking et al. (1999) posed as an open research question the cause of this phenomenon and proposed five possible explanatory hypotheses. In this paper, we present a mathematical analysis that sheds some light on these hypotheses and complements the experimental work of Hawking and Robertson (2003). We will also introduce  C   (  L ) , contamination at  L , the number of irrelevant documents amongst the top  L  relevant documents, and describe its properties.Our analysis shows that while  P   (  K )  typically will increase with collection size, the phenomenon is not universal. That is, the asymptotic behavior of  P   (  K )  and  C   (  L )  depends on the score distributions and relative proportions of relevant and irrelevant documents in the collection.},
journal = {Inf. Retr.},
month = jun,
pages = {273–294},
numpages = {22},
keywords = {very large corpus IR, precision at K, extreme value theory}
}

@article{10.1007/s10791-006-0837-9,
author = {Chang, Youjin and Kim, Minkoo and Raghavan, Vijay V.},
title = {Construction of Query Concepts Based on Feature Clustering of Documents},
year = {2006},
issue_date = {June      2006},
publisher = {Kluwer Academic Publishers},
address = {USA},
volume = {9},
number = {3},
issn = {1386-4564},
url = {https://doi.org/10.1007/s10791-006-0837-9},
doi = {10.1007/s10791-006-0837-9},
abstract = {In Information Retrieval, since it is hard to identify users' information needs, many approaches have been tried to solve this problem by expanding initial queries and reweighting the terms in the expanded queries using users' relevance judgments. Although relevance feedback is most effective when relevance information about retrieved documents is provided by users, it is not always available. Another solution is to use correlated terms for query expansion. The main problem with this approach is how to construct the term-term correlations that can be used effectively to improve retrieval performance. In this study, we try to construct  query concepts  that denote users' information needs from a document space, rather than to reformulate initial queries using the term correlations and/or users' relevance feedback. To form  query concepts , we extract features from each document, and then cluster the features into primitive concepts that are then used to form  query concepts . Experiments are performed on the Associated Press (AP) dataset taken from the TREC collection. The experimental evaluation shows that our proposed framework called QCM (Query Concept Method) outperforms baseline probabilistic retrieval model on TREC retrieval.},
journal = {Inf. Retr.},
month = jun,
pages = {231–248},
numpages = {18},
keywords = {query concepts, query reformulation, concept-based information retrieval}
}

@article{10.1007/s10791-006-7150-5,
author = {Tang, Thanh Tin and Craswell, Nick and Hawking, David and Griffiths, Kathy and Christensen, Helen},
title = {Quality and Relevance of Domain-Specific Search: A Case Study in Mental Health},
year = {2006},
issue_date = {March     2006},
publisher = {Kluwer Academic Publishers},
address = {USA},
volume = {9},
number = {2},
issn = {1386-4564},
url = {https://doi.org/10.1007/s10791-006-7150-5},
doi = {10.1007/s10791-006-7150-5},
abstract = {When searching for health information, results quality can be judged against available scientific evidence: Do search engines return advice consistent with evidence based medicine? We compared the performance of domain-specific health and depression search engines against a general-purpose engine (Google) on both relevance of results and quality of advice. Over 101 queries, to which the term `depression' was added if not already present, Google returned more relevant results than those of the domain-specific engines. However, over the 50 treatment-related queries, Google returned 70 pages recommending for or against a well studied treatment, of which 19 strongly disagreed with the scientific evidence. A domain-specific index of 4 sites selected by domain experts was only wrong in 5 of 50 recommendations. Analysis suggests a tension between relevance and quality. Indexing more pages can give a greater number of relevant results, but selective inclusion can give better quality.},
journal = {Inf. Retr.},
month = mar,
pages = {207–225},
numpages = {19},
keywords = {Mental health, Domain specific search, Depression, Focused crawling}
}

@article{10.1007/s10791-006-7149-y,
author = {Soricut, Radu and Brill, Eric},
title = {Automatic Question Answering Using the Web: Beyond the Factoid},
year = {2006},
issue_date = {March     2006},
publisher = {Kluwer Academic Publishers},
address = {USA},
volume = {9},
number = {2},
issn = {1386-4564},
url = {https://doi.org/10.1007/s10791-006-7149-y},
doi = {10.1007/s10791-006-7149-y},
abstract = {In this paper we describe and evaluate a Question Answering (QA) system that goes beyond answering factoid questions. Our approach to QA assumes no restrictions on the type of questions that are handled, and no assumption that the answers to be provided are factoids. We present an unsupervised approach for collecting question and answer pairs from FAQ pages, which we use to collect a corpus of 1 million question/answer pairs from FAQ pages available on the Web. This corpus is used to train various statistical models employed by our QA system: a statistical chunker used to transform a natural language-posed question into a phrase-based query to be submitted for exact match to an off-the-shelf search engine; an answer/question translation model, used to assess the likelihood that a proposed answer is indeed an answer to the posed question; and an answer language model, used to assess the likelihood that a proposed answer is a well-formed answer. We evaluate our QA system in a modular fashion, by comparing the performance of baseline algorithms against our proposed algorithms for various modules in our QA system. The evaluation shows that our system achieves reasonable performance in terms of answer accuracy for a large variety of complex, non-factoid questions.},
journal = {Inf. Retr.},
month = mar,
pages = {191–206},
numpages = {16}
}

@article{10.1007/s10791-006-7148-z,
author = {Smyth, Barry and Balfe, Evelyn},
title = {Anonymous Personalization in Collaborative Web Search},
year = {2006},
issue_date = {March     2006},
publisher = {Kluwer Academic Publishers},
address = {USA},
volume = {9},
number = {2},
issn = {1386-4564},
url = {https://doi.org/10.1007/s10791-006-7148-z},
doi = {10.1007/s10791-006-7148-z},
abstract = {We present an innovative approach to Web search, called  collaborative search , that seeks to cope with the type of vague queries that are commonplace in Web search. We do this by leveraging the search behaviour of previous searchers to personalize future result-lists according to the implied preferences of a community of like-minded individuals. This technique is implemented in the I-SPY meta-search engine and we present the results of a live-user trial which indicates that I-SPY can offer improved search performance when compared to a benchmark search engine, across a variety of performance metrics. In addition, I-SPY achieves its level of personalization while preserving the anonymity of individual users, and we argue that this offers unique privacy benefits compared to alternative approaches to personalization.},
journal = {Inf. Retr.},
month = mar,
pages = {165–190},
numpages = {26},
keywords = {Social search, Personalization, Web search}
}

@article{10.1007/s10791-006-7147-0,
author = {Plachouras, Vassilis and Cacheda, Fidel and Ounis, Iadh},
title = {A Decision Mechanism for the Selective Combination of Evidence in Topic Distillation},
year = {2006},
issue_date = {March     2006},
publisher = {Kluwer Academic Publishers},
address = {USA},
volume = {9},
number = {2},
issn = {1386-4564},
url = {https://doi.org/10.1007/s10791-006-7147-0},
doi = {10.1007/s10791-006-7147-0},
abstract = {The combination of evidence can increase retrieval effectiveness. In this paper, we investigate the effectiveness of a decision mechanism for the selective combination of evidence for Web Information Retrieval and particularly for topic distillation. We introduce two measures of a query's broadness and use them to select an appropriate combination of evidence for each query. The results from our experiments show that there is a statistically significant association between the output of the decision mechanism and the relative effectiveness of the different combinations of evidence. Moreover, we show that the proposed methodology can be applied in an operational setting, where relevance information is not available, by setting the decision mechanism's thresholds automatically.},
journal = {Inf. Retr.},
month = mar,
pages = {139–163},
numpages = {25},
keywords = {Decision mechanism, Selective combination of evidence, Aggregates, Query scope, Web information retrieval, Topic distillation}
}

@article{10.1007/s10791-006-7146-1,
author = {Broder, A. Z. and Lempel, R. and Maghoul, F. and Pedersen, J.},
title = {Efficient PageRank Approximation via Graph Aggregation},
year = {2006},
issue_date = {March     2006},
publisher = {Kluwer Academic Publishers},
address = {USA},
volume = {9},
number = {2},
issn = {1386-4564},
url = {https://doi.org/10.1007/s10791-006-7146-1},
doi = {10.1007/s10791-006-7146-1},
abstract = {We present a framework for approximating random-walk based probability distributions over Web pages using graph aggregation. The basic idea is to partition the graph into classes of quasi-equivalent vertices, to project the page-based random walk to be approximated onto those classes, and to compute the stationary probability distribution of the resulting class-based random walk. From this distribution we can quickly reconstruct a distribution on pages. In particular, our framework can approximate the well-known PageRank distribution by setting the classes according to the set of pages on each Web host.We experimented on a Web-graph containing over 1.4 billion pages and over 6.6 billion links from a crawl of the Web conducted by AltaVista in September 2003. We were able to produce a ranking that has Spearman rank-order correlation of 0.95 with respect to PageRank. The clock time required by a simplistic implementation of our method was less than half the time required by a highly optimized implementation of PageRank, implying that larger speedup factors are probably possible.},
journal = {Inf. Retr.},
month = mar,
pages = {123–138},
numpages = {16},
keywords = {Citation and link analysis, Web IR}
}

@article{10.1007/s10791-006-7145-2,
author = {Melucci, Massimo and Hawking, David},
title = {Introduction: A Perspective on Web Information Retrieval},
year = {2006},
issue_date = {March     2006},
publisher = {Kluwer Academic Publishers},
address = {USA},
volume = {9},
number = {2},
issn = {1386-4564},
url = {https://doi.org/10.1007/s10791-006-7145-2},
doi = {10.1007/s10791-006-7145-2},
journal = {Inf. Retr.},
month = mar,
pages = {119–122},
numpages = {4}
}

@article{10.1007/s10791-005-5724-2,
author = {Soergel, Dagobert},
title = {Information Representation and Retrieval in the Digital Age (ASIST Monograph Series) by Heting Chu, Medford, NJ: Information Today; 2003. 248 p. ISBN 1-57387-172-9},
year = {2006},
issue_date = {January   2006},
publisher = {Kluwer Academic Publishers},
address = {USA},
volume = {9},
number = {1},
issn = {1386-4564},
url = {https://doi.org/10.1007/s10791-005-5724-2},
doi = {10.1007/s10791-005-5724-2},
journal = {Inf. Retr.},
month = jan,
pages = {111–112},
numpages = {2}
}

@article{10.1007/s10791-005-5723-3,
author = {Coden, Anni R. and Brown, Eric W.},
title = {Automatic Search from Streaming Data},
year = {2006},
issue_date = {January   2006},
publisher = {Kluwer Academic Publishers},
address = {USA},
volume = {9},
number = {1},
issn = {1386-4564},
url = {https://doi.org/10.1007/s10791-005-5723-3},
doi = {10.1007/s10791-005-5723-3},
abstract = {Streaming data poses a variety of new and interesting challenges for information retrieval and text analysis. Unlike static document collections, which are typically analyzed and indexed off-line to support ad-hoc queries, streaming data often must be analyzed on the fly and acted on as the data passes through the analysis system. Speech is one example of streaming data that is a challenge to exploit, yet has significant potential to provide value in a knowledge management system. We are specifically interested in techniques that analyze streaming data and automatically find  collateral information , or information that clarifies, expands, and generally enhances the value of the streaming data. We present a system that analyzes a data stream and automatically finds documents related to the current topic of discussion in the data stream. Experimental results show that the system generates result lists with an average precision at 10 hits of better than 60%. We also present a hit-list re-ranking technique based on named entity analysis and automatic text categorization that can improve the search results by 6%---12%.},
journal = {Inf. Retr.},
month = jan,
pages = {95–109},
numpages = {15},
keywords = {Speech retrieval, Text mining, Information retrieval}
}

@article{10.1007/s10791-005-5722-4,
author = {Mart\'{\i}nez-Santiago, Fernando and Ure\~{n}a-L\'{o}pez, L. Alfonso and Mart\'{\i}n-Valdivia, Maite},
title = {A Merging Strategy Proposal: The 2-Step Retrieval Status Value Method},
year = {2006},
issue_date = {January   2006},
publisher = {Kluwer Academic Publishers},
address = {USA},
volume = {9},
number = {1},
issn = {1386-4564},
url = {https://doi.org/10.1007/s10791-005-5722-4},
doi = {10.1007/s10791-005-5722-4},
abstract = {A usual strategy to implement CLIR (Cross-Language Information Retrieval) systems is the so-called query translation approach. The user query is translated for each language present in the multilingual collection in order to compute an independent monolingual information retrieval process per language. Thus, this approach divides documents according to language. In this way, we obtain as many different collections as languages. After searching in these corpora and obtaining a result list per language, we must merge them in order to provide a single list of retrieved articles.In this paper, we propose an approach to obtain a single list of relevant documents for CLIR systems driven by query translation. This approach, which we call 2-step RSV (RSV: Retrieval Status Value), is based on the re-indexing of the retrieval documents according to the query vocabulary, and it performs noticeably better than traditional methods.The proposed method requires query vocabulary alignment: given a word for a given query, we must know the translation or translations to the other languages. Because this is not always possible, we have researched on a mixed model. This mixed model is applied in order to deal with queries with partial word-level alignment. The results prove that even in this scenario, 2-step RSV performs better than traditional merging methods.},
journal = {Inf. Retr.},
month = jan,
pages = {71–93},
numpages = {23},
keywords = {CLIR, Pseudo-relevance feedback, 2-step RSV, Mixed 2-step RSV, Merging strategies}
}

@article{10.1007/s10791-005-5721-5,
author = {Fuhr, Norbert and G\"{o}vert, Norbert},
title = {Retrieval Quality vs. Effectiveness of Specificity-Oriented Search in XML Collections},
year = {2006},
issue_date = {January   2006},
publisher = {Kluwer Academic Publishers},
address = {USA},
volume = {9},
number = {1},
issn = {1386-4564},
url = {https://doi.org/10.1007/s10791-005-5721-5},
doi = {10.1007/s10791-005-5721-5},
abstract = {Content-only queries in hierarchically structured documents should retrieve the most specific document nodes which are exhaustive to the information need. For this problem, we investigate two methods of augmentation, which both yield high retrieval quality. As retrieval effectiveness, we consider the ratio of retrieval quality and response time; thus, fast approximations to the 'correct' retrieval result may yield higher effectiveness. We present a classification scheme for algorithms addressing this issue, and adopt known algorithms from standard document retrieval for XML retrieval. As a new strategy, we propose  incremental-interruptible retrieval , which allows for instant presentation of the top ranking documents. We develop a new algorithm implementing this strategy and evaluate the different methods with the INEX collection.},
journal = {Inf. Retr.},
month = jan,
pages = {55–70},
numpages = {16},
keywords = {Efficiency, Content-only search, Ranked retrieval, Incremental algorithm, XML retrieval}
}

@article{10.1007/s10791-005-5720-6,
author = {Korenius, Tuomo and Laurikkala, Jorma and Juhola, Martti and J\"{a}rvelin, Kalervo},
title = {Hierarchical Clustering of a Finnish Newspaper Article Collection with Graded Relevance Assessments},
year = {2006},
issue_date = {January   2006},
publisher = {Kluwer Academic Publishers},
address = {USA},
volume = {9},
number = {1},
issn = {1386-4564},
url = {https://doi.org/10.1007/s10791-005-5720-6},
doi = {10.1007/s10791-005-5720-6},
abstract = {Search facilitated with agglomerative hierarchical clustering methods was studied in a collection of Finnish newspaper articles (  N  = 53,893). To allow quick experiments, clustering was applied to a sample (  N  = 5,000) that was reduced with principal components analysis. The dendrograms were heuristically cut to find an optimal partition, whose clusters were compared with each of the 30 queries to retrieve the best-matching cluster. The four-level relevance assessment was collapsed into a binary one by (A) considering all the relevant and (B) only the highly relevant documents relevant, respectively. Single linkage (SL) was the worst method. It created many tiny clusters, and, consequently, searches enabled with it had high precision and low recall. The complete linkage (CL), average linkage (AL), and Ward's methods (WM) returned reasonably-sized clusters typically of 18---32 documents. Their recall (A: 27---52%, B: 50---82%) and precision (A: 83---90%, B: 18---21%) was higher than and comparable to those of the SL clusters, respectively. The AL and WM clustering had 1---8% better effectiveness than nearest neighbor searching (NN), and SL and CL were 1---9% less efficient that NN. However, the differences were statistically insignificant. When evaluated with the liberal assessment A, the results suggest that the AL and WM clustering offer better retrieval ability than NN. Assessment B renders the AL and WM clustering better than NN, when recall is considered more important than precision. The results imply that collections in the highly inflectional and agglutinative languages, such as Finnish, may be clustered as the collections in English, provided that documents are appropriately preprocessed.},
journal = {Inf. Retr.},
month = jan,
pages = {33–53},
numpages = {21},
keywords = {Hierarchical clustering, Graded relevance, Principal components analysis, Finnish language}
}

@article{10.1007/s10791-005-5719-z,
author = {Yang, Hui and Zhang, Minjie},
title = {Two-Stage Statistical Language Models for Text Database Selection},
year = {2006},
issue_date = {January   2006},
publisher = {Kluwer Academic Publishers},
address = {USA},
volume = {9},
number = {1},
issn = {1386-4564},
url = {https://doi.org/10.1007/s10791-005-5719-z},
doi = {10.1007/s10791-005-5719-z},
abstract = {As the number and diversity of distributed Web databases on the Internet exponentially increase, it is difficult for user to know which databases are appropriate to search. Given database language models that describe the content of each database, database selection services can provide assistance in locating databases relevant to the information needs of users. In this paper, we propose a database selection approach based on statistical language modeling. The basic idea behind the approach is that, for databases that are categorized into a topic hierarchy, individual language models are estimated at different search stages, and then the databases are ranked by the similarity to the query according to the estimated language model. Two-stage smoothed language models are presented to circumvent inaccuracy due to word sparseness. Experimental results demonstrate that such a language modeling approach is competitive with current state-of-the-art database selection approaches.},
journal = {Inf. Retr.},
month = jan,
pages = {5–31},
numpages = {27},
keywords = {Statistical language modeling, Text database selection, Hierarchical topics, Query expansion, Database language model, Distributed information retrieval}
}

@article{10.1007/s10791-005-0751-6,
author = {Piwowarski, Benjamin and Gallinari, Patrick},
title = {A Bayesian Framework for XML Information Retrieval: Searching and Learning with the INEX Collection},
year = {2005},
issue_date = {December  2005},
publisher = {Kluwer Academic Publishers},
address = {USA},
volume = {8},
number = {4},
issn = {1386-4564},
url = {https://doi.org/10.1007/s10791-005-0751-6},
doi = {10.1007/s10791-005-0751-6},
abstract = {Most recent document standards like XML rely on structured representations. On the other hand, current information retrieval systems have been developed for flat document representations and cannot be easily extended to cope with more complex document types. The design of such systems is still an open problem. We present a new model for structured document retrieval which allows computing scores of document parts. This model is based on Bayesian networks whose conditional probabilities are learnt from a labelled collection of structured documents--which is composed of documents, queries and their associated assessments. Training these models is a complex machine learning task and is not standard. This is the focus of the paper: we propose here to train the structured Bayesian Network model using a cross-entropy training criterion. Results are presented on the INEX corpus of XML documents.},
journal = {Inf. Retr.},
month = dec,
pages = {655–681},
numpages = {27},
keywords = {XML, machine learning for structured retrieval, Bayesian Networks, structured information retrieval}
}

@article{10.1007/s10791-005-0750-7,
author = {Kamps, Jaap and Rijke, Maarten De and Sigurbj\"{o}rnsson, B\"{o}rkur},
title = {The Importance of Length Normalization for XML Retrieval},
year = {2005},
issue_date = {December  2005},
publisher = {Kluwer Academic Publishers},
address = {USA},
volume = {8},
number = {4},
issn = {1386-4564},
url = {https://doi.org/10.1007/s10791-005-0750-7},
doi = {10.1007/s10791-005-0750-7},
abstract = {XML retrieval is a departure from standard document retrieval in which each individual XML element, ranging from italicized words or phrases to full blown articles, is a retrievable unit. The distribution of XML element lengths is unlike what we usually observe in standard document collections, prompting us to revisit the issue of document length normalization. We perform a comparative analysis of arbitrary elements versus relevant elements, and show the importance of element length as a parameter for XML retrieval. Within the language modeling framework, we investigate a range of techniques that deal with length either directly or indirectly. We observe a length-bias introduced by the amount of smoothing, and show the importance of extreme length bias for XML retrieval. We also show that simply removing shorter elements from the index (by introducing a cut-off value) does not create an appropriate element length normalization. Even after restricting the minimal size of XML elements occurring in the index, the importance of an extreme explicit length bias remains.},
journal = {Inf. Retr.},
month = dec,
pages = {631–654},
numpages = {24},
keywords = {smoothing, XML retrieval, length normalization, language models}
}

@article{10.1007/s10791-005-0749-0,
author = {Larson, Ray R.},
title = {A Fusion Approach to XML Structured Document Retrieval},
year = {2005},
issue_date = {December  2005},
publisher = {Kluwer Academic Publishers},
address = {USA},
volume = {8},
number = {4},
issn = {1386-4564},
url = {https://doi.org/10.1007/s10791-005-0749-0},
doi = {10.1007/s10791-005-0749-0},
abstract = {In this paper we evaluate the application of data fusion or meta-search methods, combining different algorithms and XML elements, to content-oriented retrieval of XML structured data. The primary approach is the combination of a probabilistic methods using Logistic regression and the Okapi BM-25 algorithm for estimation of document relevance or XML element relevance, in conjunction with Boolean approaches for some query elements. In the evaluation we use the INEX XML test collection to examine the relative performance of individual algorithms and elements and compare these to the performance of the data fusion approaches.},
journal = {Inf. Retr.},
month = dec,
pages = {601–629},
numpages = {29},
keywords = {data fusion, XML retrieval, probabilistic retrieval, logistic regression}
}

@article{10.1007/s10791-005-0748-1,
author = {Pehcevski, Jovan and Thom, James A. and Vercoustre, Anne-Marie},
title = {Hybrid XML Retrieval: Combining Information Retrieval and a Native XML Database},
year = {2005},
issue_date = {December  2005},
publisher = {Kluwer Academic Publishers},
address = {USA},
volume = {8},
number = {4},
issn = {1386-4564},
url = {https://doi.org/10.1007/s10791-005-0748-1},
doi = {10.1007/s10791-005-0748-1},
abstract = {This paper investigates the impact of three approaches to XML retrieval: using Zettair, a full-text information retrieval system; using eXist, a native XML database; and using a hybrid system that takes full article answers from Zettair and uses eXist to extract elements from those articles. For the content-only topics, we undertake a preliminary analysis of the INEX 2003 relevance assessments in order to identify the types of highly relevant document components. Further analysis identifies two complementary sub-cases of relevance assessments (  General  and  Specific ) and two categories of topics (  Broad  and  Narrow ). We develop a novel retrieval module that for a content-only topic utilises the information from the resulting answer list of a native XML database and dynamically determines the preferable units of retrieval, which we call  Coherent Retrieval Elements.  The results of our experiments show that--when each of the three systems is evaluated against different retrieval scenarios (such as different cases of relevance assessments, different topic categories and different choices of evaluation metrics)--the XML retrieval systems exhibit varying behaviour and the best performance can be reached for different values of the retrieval parameters. In the case of INEX 2003 relevance assessments for the content-only topics, our newly developed hybrid XML retrieval system is substantially more effective than either Zettair or eXist, and yields a robust and a very effective XML retrieval.},
journal = {Inf. Retr.},
month = dec,
pages = {571–600},
numpages = {30},
keywords = {Zettair, eXist, XML information retrieval, XML databases, INEX}
}

@article{10.1007/s10791-005-0747-2,
author = {List, Johan and Mihajlovic, Vojkan and Ram\'{\i}rez, Georgina and Vries, Arjen P. and Hiemstra, Djoerd and Blok, Henk Ernst},
title = {TIJAH: Embracing IR Methods in XML Databases},
year = {2005},
issue_date = {December  2005},
publisher = {Kluwer Academic Publishers},
address = {USA},
volume = {8},
number = {4},
issn = {1386-4564},
url = {https://doi.org/10.1007/s10791-005-0747-2},
doi = {10.1007/s10791-005-0747-2},
abstract = {This paper discusses our participation in INEX (the Initiative for the Evaluation of XML Retrieval) using the TIJAH XML-IR system. TIJAH's system design follows a `standard' layered database architecture, carefully separating the conceptual, logical and physical levels. At the conceptual level, we classify the INEX XPath-based query expressions into three different query patterns. For each pattern, we present its mapping into a query execution strategy. The logical layer exploits  score region algebra  (SRA) as the basis for query processing. We discuss the region operators used to select and manipulate XML document components. The logical algebra expressions are mapped into efficient relational algebra expressions over a physical representation of the XML document collection using the `pre-post numbering scheme'. The paper concludes with an analysis of experiments performed with the INEX test collection.},
journal = {Inf. Retr.},
month = dec,
pages = {547–570},
numpages = {24},
keywords = {structured document retrieval}
}

@article{10.1007/s10791-005-0746-3,
author = {Schenkel, Ralf and Theobald, Anja and Weikum, Gerhard},
title = {Semantic Similarity Search on Semistructured Data with the XXL Search Engine},
year = {2005},
issue_date = {December  2005},
publisher = {Kluwer Academic Publishers},
address = {USA},
volume = {8},
number = {4},
issn = {1386-4564},
url = {https://doi.org/10.1007/s10791-005-0746-3},
doi = {10.1007/s10791-005-0746-3},
abstract = {Query languages for XML such as XPath or XQuery support Boolean retrieval: a query result is a (possibly restructured) subset of XML elements or entire documents that satisfy the search conditions of the query. This search paradigm works for highly schematic XML data collections such as electronic catalogs. However, for searching information in open environments such as the Web or intranets of large corporations, ranked retrieval is more appropriate: a query result is a ranked list of XML elements in descending order of (estimated) relevance. Web search engines, which are based on the ranked retrieval paradigm, do, however, not consider the additional information and rich annotations provided by the structure of XML documents and their element names.This article presents the XXL search engine that supports relevance ranking on XML data. XXL is particularly geared for path queries with wildcards that can span multiple XML collections and contain both exact-match as well as semantic-similarity search conditions. In addition, ontological information and suitable index structures are used to improve the search efficiency and effectiveness. XXL is fully implemented as a suite of Java classes and servlets. Experiments in the context of the INEX benchmark demonstrate the efficiency of the XXL search engine and underline its effectiveness for ranked retrieval.},
journal = {Inf. Retr.},
month = dec,
pages = {521–545},
numpages = {25},
keywords = {semistructured data, XML retrieval, ranked retrieval}
}

@article{10.1007/s10791-005-0745-4,
author = {Fuhr, Norbert and Lalmas, Mounia},
title = {Introduction to the Special Issue on INEX},
year = {2005},
issue_date = {December  2005},
publisher = {Kluwer Academic Publishers},
address = {USA},
volume = {8},
number = {4},
issn = {1386-4564},
url = {https://doi.org/10.1007/s10791-005-0745-4},
doi = {10.1007/s10791-005-0745-4},
abstract = {This special issue contains articles describing XML retrieval approaches developed and evaluated during the second year of INEX, the evaluation initiative for XML retrieval.},
journal = {Inf. Retr.},
month = dec,
pages = {515–519},
numpages = {5}
}

@article{10.1007/s10791-005-6996-2,
author = {Ivory, Melody Y.},
title = {Mapping Scientific Frontiers: The Quest for Knowledge Visualization by Chaomei Chen. New York: Springer Verlag; 2003, 256 Pps, $79.95 (ISBN: 0-85233-494-0)},
year = {2005},
issue_date = {May 2005},
publisher = {Kluwer Academic Publishers},
address = {USA},
volume = {8},
number = {3},
issn = {1386-4564},
url = {https://doi.org/10.1007/s10791-005-6996-2},
doi = {10.1007/s10791-005-6996-2},
journal = {Inf. Retr.},
month = may,
pages = {505–507},
numpages = {3}
}

@article{10.1007/s10791-005-6995-3,
author = {Metzler, Donald and Croft, W. Bruce},
title = {Analysis of Statistical Question Classification for Fact-Based Questions},
year = {2005},
issue_date = {May 2005},
publisher = {Kluwer Academic Publishers},
address = {USA},
volume = {8},
number = {3},
issn = {1386-4564},
url = {https://doi.org/10.1007/s10791-005-6995-3},
doi = {10.1007/s10791-005-6995-3},
abstract = {Question classification systems play an important role in question answering systems and can be used in a wide range of other domains. The goal of question classification is to accurately assign labels to questions based on expected answer type. Most approaches in the past have relied on matching questions against hand-crafted rules. However, rules require laborious effort to create and often suffer from being too specific. Statistical question classification methods overcome these issues by employing machine learning techniques. We empirically show that a statistical approach is robust and achieves good performance on three diverse data sets with little or no hand tuning. Furthermore, we examine the role different syntactic and semantic features have on performance. We find that semantic features tend to increase performance more than purely syntactic features. Finally, we analyze common causes of misclassification error and provide insight into ways they may be overcome.},
journal = {Inf. Retr.},
month = may,
pages = {481–504},
numpages = {24},
keywords = {machine learning, question classification, semantic features, Support Vector Machines, WordNet, syntactic features, question answering}
}

@article{10.1007/s10791-005-6994-4,
author = {Frank Hsu, D. and Taksa, Isak},
title = {Comparing Rank and Score Combination Methods for Data Fusion in Information Retrieval},
year = {2005},
issue_date = {May 2005},
publisher = {Kluwer Academic Publishers},
address = {USA},
volume = {8},
number = {3},
issn = {1386-4564},
url = {https://doi.org/10.1007/s10791-005-6994-4},
doi = {10.1007/s10791-005-6994-4},
abstract = {Combination of multiple evidences (multiple query formulations, multiple retrieval schemes or systems) has been shown (mostly experimentally) to be effective in data fusion in information retrieval. However, the question of why and how combination should be done still remains largely unanswered. In this paper, we provide a model for simulation and a framework for analysis in the study of data fusion in the information retrieval domain. A  rank/score function  is defined and the concept of a Cayley graph is used in the design and analysis of our framework. The model and framework have led us to better understanding of the data fusion phenomena in information retrieval. In particular, by exploiting the graphical properties of the rank/score function, we have shown analytically and by simulation that combination using rank performs better than combination using score under certain conditions. Moreover, we demonstrated that the rank/score function might be used as a predictive variable for the effectiveness of combination of multiple evidences.},
journal = {Inf. Retr.},
month = may,
pages = {449–480},
numpages = {32},
keywords = {symmetric group, Cayley graphs and digraphs, information retrieval (IR), multiple evidences, data fusion (DF), permutation, evidence combinations, rank combination, rank/score function, score combination}
}

@article{10.1007/s10791-005-6993-5,
author = {Srinivasan, P. and Menczer, F. and Pant, G.},
title = {A General Evaluation Framework for Topical Crawlers},
year = {2005},
issue_date = {May 2005},
publisher = {Kluwer Academic Publishers},
address = {USA},
volume = {8},
number = {3},
issn = {1386-4564},
url = {https://doi.org/10.1007/s10791-005-6993-5},
doi = {10.1007/s10791-005-6993-5},
abstract = {Topical crawlers are becoming important tools to support applications such as specialized Web portals, online searching, and competitive intelligence. As the Web mining field matures, the disparate crawling strategies proposed in the literature will have to be evaluated and compared on common tasks through well-defined performance measures. This paper presents a general framework to evaluate topical crawlers. We identify a class of tasks that model crawling applications of different nature and difficulty. We then introduce a set of performance measures for fair comparative evaluations of crawlers along several dimensions including generalized notions of precision, recall, and efficiency that are appropriate and practical for the Web. The framework relies on independent relevance judgements compiled by human editors and available from public directories. Two sources of evidence are proposed to assess crawled pages, capturing different relevance criteria. Finally we introduce a set of topic characterizations to analyze the variability in crawling effectiveness across topics. The proposed evaluation framework synthesizes a number of methodologies in the topical crawlers literature and many lessons learned from several studies conducted by our group. The general framework is described in detail and then illustrated in practice by a case study that evaluates four public crawling algorithms. We found that the proposed framework is effective at evaluating, comparing, differentiating and interpreting the performance of the four crawlers. For example, we found the IS crawler to be most sensitive to the popularity of topics.},
journal = {Inf. Retr.},
month = may,
pages = {417–447},
numpages = {31},
keywords = {precision, tasks, efficiency, evaluation, recall, Web crawlers, topics}
}

@article{10.1007/s10791-005-6992-6,
author = {Das, Subrata and Shuster, Kurt and Wu, Curt and Levit, Igor},
title = {Mobile Agents for Distributed and Heterogeneous Information Retrieval},
year = {2005},
issue_date = {May 2005},
publisher = {Kluwer Academic Publishers},
address = {USA},
volume = {8},
number = {3},
issn = {1386-4564},
url = {https://doi.org/10.1007/s10791-005-6992-6},
doi = {10.1007/s10791-005-6992-6},
abstract = {The heterogeneous, distributed and voluminous nature of many government and corporate data sources impose severe constraints on meeting the diverse requirements of users who analyze the data. Additionally, communication bandwidth limitations, time constraints, and multiple data formats impose further restrictions on users of these distributed data sources. In this paper, we present an Agent-based Complex QUerying and Information Retrieval Engine (ACQUIRE) for large, heterogeneous, and distributed data sources. ACQUIRE acts as a softbot or interface agent by presenting users with a view of a single, unified, homogenous data source, against which users can pose high-level declarative queries. ACQUIRE translates each such user query into a set of sub-queries by employing a combination of planning and traditional database query optimization techniques. ACQUIRE then spawns a set of mobile agents corresponding to these sub-queries, which in turn retrieve the data from various distributed data sources by dynamically optimizing the retrieval strategy as it is carried out. These mobile agents carry with them data-processing code that can be executed at the remote site, thus reducing the size of data returned by the agent. When all mobile agents have returned, ACQUIRE filters and merges the retrieved data and presents the results to the user. While the system is still very much a work in progress, current validation experiments on simulated NASA Distributed Active Archive Centers (DAACs) have demonstrated that complex queries can be effectively decomposed and retrieved by this approach.},
journal = {Inf. Retr.},
month = may,
pages = {383–416},
numpages = {34},
keywords = {mobile agents, query optimization, information retrieval, query planning, heterogeneous and distributed data}
}

@article{10.1007/s10791-005-6991-7,
author = {Trotman, Andrew},
title = {Learning to Rank},
year = {2005},
issue_date = {May 2005},
publisher = {Kluwer Academic Publishers},
address = {USA},
volume = {8},
number = {3},
issn = {1386-4564},
url = {https://doi.org/10.1007/s10791-005-6991-7},
doi = {10.1007/s10791-005-6991-7},
abstract = {New general purpose ranking functions are discovered using genetic programming. The TREC WSJ collection was chosen as a training set. A baseline comparison function was chosen as the best of inner product, probability, cosine, and Okapi BM25. An elitist genetic algorithm with a population size 100 was run 13 times for 100 generations and the best performing algorithms chosen from these. The best learned functions, when evaluated against the best baseline function (BM25), demonstrate some significant performance differences, with improvements in mean average precision as high as 32% observed on one TREC collection not used in training. In no test is BM25 shown to significantly outperform the best learned function.},
journal = {Inf. Retr.},
month = may,
pages = {359–381},
numpages = {23},
keywords = {document ranking, machine learning, genetic programming, searching}
}

@article{10.1007/s10791-005-6047-z,
author = {Dominich, S\'{a}ndor and Lalmas, Mounia and Rijsbergen, Cornelius Joost (Keith)},
title = {Guest Editorial: Spaces, Logic, and Link Analysis in IR: Recent Advances From A Mathematical and Logical Perspective},
year = {2005},
issue_date = {April     2005},
publisher = {Kluwer Academic Publishers},
address = {USA},
volume = {8},
number = {2},
issn = {1386-4564},
url = {https://doi.org/10.1007/s10791-005-6047-z},
doi = {10.1007/s10791-005-6047-z},
journal = {Inf. Retr.},
month = apr,
pages = {175–179},
numpages = {5}
}

@article{10.1007/s10791-005-5666-8,
author = {Kogan, Jacob and Teboulle, Marc and Nicholas, Charles},
title = {Data Driven Similarity Measures for K-Means Like Clustering Algorithms},
year = {2005},
issue_date = {April     2005},
publisher = {Kluwer Academic Publishers},
address = {USA},
volume = {8},
number = {2},
issn = {1386-4564},
url = {https://doi.org/10.1007/s10791-005-5666-8},
doi = {10.1007/s10791-005-5666-8},
abstract = {We present an optimization approach that generates  k -means like clustering algorithms. The batch  k -means and the incremental  k -means are two well known versions of the classical  k -means clustering algorithm (Duda et al. 2000). To benefit from the speed of the batch version and the accuracy of the incremental version we combine the two in a "ping--pong" fashion. We use a distance-like function that combines the squared Euclidean distance with relative entropy. In the extreme cases our algorithm recovers the classical  k -means clustering algorithm and generalizes the Divisive Information Theoretic clustering algorithm recently reported independently by Berkhin and Becher (2002) and Dhillon1 et al. (2002). Results of numerical experiments that demonstrate the viability of our approach are reported.},
journal = {Inf. Retr.},
month = apr,
pages = {331–349},
numpages = {19},
keywords = {optimization, entropy, clustering algorithms}
}

@article{10.1007/s10791-005-5665-9,
author = {Robertson, Stephen},
title = {On Event Spaces and Probabilistic Models in Information Retrieval},
year = {2005},
issue_date = {April     2005},
publisher = {Kluwer Academic Publishers},
address = {USA},
volume = {8},
number = {2},
issn = {1386-4564},
url = {https://doi.org/10.1007/s10791-005-5665-9},
doi = {10.1007/s10791-005-5665-9},
abstract = {A basic notion of probability theory is the event space, on which the probability measure is defined. A probabilistic model needs an event space. However, some classes of events (which we may want to model probabilistically) exhibit structure which does not fit well into the traditional event space notion. A simple one-to-many example is discussed at length. The information retrieval case, involving queries, documents and relevance, is analysed. The event space issue makes for some difficulty in comparing different probabilistic models in IR.},
journal = {Inf. Retr.},
month = apr,
pages = {319–329},
numpages = {11},
keywords = {information retrieval, event space, probabilistic models}
}

@article{10.1007/s10791-005-5664-x,
author = {Bordogna, Gloria and Pasi, Gabriella},
title = {Personalised Indexing and Retrieval of Heterogeneous Structured Documents},
year = {2005},
issue_date = {April     2005},
publisher = {Kluwer Academic Publishers},
address = {USA},
volume = {8},
number = {2},
issn = {1386-4564},
url = {https://doi.org/10.1007/s10791-005-5664-x},
doi = {10.1007/s10791-005-5664-x},
abstract = {In this paper the problem of indexing heterogeneous structured documents and of retrieving semi-structured documents is considered. We propose a flexible paradigm for both indexing such documents and formulating user queries specifying soft constraints on both documents' structure and content. At the indexing level we propose a model that achieves flexibility by constructing personalised document representations based on users' views of the documents. This is obtained by allowing users to specify their preferences on the documents' sections that they estimate to bear the most interesting information, as well as to linguistically quantify the number of sections which determine the global potential interest of the documents. At the query language level, a flexible query language for expressing soft selection conditions on both the documents' structure and content is proposed.},
journal = {Inf. Retr.},
month = apr,
pages = {301–318},
numpages = {18},
keywords = {heterogeneous structured documents, Ordered Weighted Averaging Operators, content-based and structure-based querying}
}

@article{10.1007/s10791-005-5663-y,
author = {Cheung, Karen S. and Vogel, Douglas},
title = {Complexity Reduction in Lattice-Based Information Retrieval},
year = {2005},
issue_date = {April     2005},
publisher = {Kluwer Academic Publishers},
address = {USA},
volume = {8},
number = {2},
issn = {1386-4564},
url = {https://doi.org/10.1007/s10791-005-5663-y},
doi = {10.1007/s10791-005-5663-y},
abstract = {Though lattice-based information representation has the advantage of providing efficient visual interface over textual display, the complexity of a lattice may grow rapidly with the size of the database. In this paper we formally draw the analogy between Vector Space Model and Concept Lattice, from which we introduce the notion of Term-Document Lattice as a model for information retrieval. We then propose to use the idea of quotient lattice to reduce the complexity of a Term-Document Lattice. The equivalence relation required to construct the quotient lattice is obtained by performing a Singular Value Decomposition on the original term-document matrix.},
journal = {Inf. Retr.},
month = apr,
pages = {285–299},
numpages = {15},
keywords = {lattice theory, information retrieval, concept lattice}
}

@article{10.1007/s10791-005-5662-z,
author = {G\'{o}th, J\'{u}lia and Skrop, Adrienn},
title = {Varying Retrieval Categoricity Using Hyperbolic Geometry},
year = {2005},
issue_date = {April     2005},
publisher = {Kluwer Academic Publishers},
address = {USA},
volume = {8},
number = {2},
issn = {1386-4564},
url = {https://doi.org/10.1007/s10791-005-5662-z},
doi = {10.1007/s10791-005-5662-z},
abstract = {The paper proposes a Vector Space Model over the Cayley-Klein Hyperbolic Geometry (referred to as Hyperbolic Information Retrieval = HIR) using a similarity measure derived from the hyperbolic distance. It is shown that the proposed model is equivalent with the classical Vector Space Model using Cosine measure with normalized weighting scheme. It is also shown that the categoricity of the new retrieval system can be varied by only modifying the radius of the hyperbolic space and without using a different weighting scheme and similarity measure, which is not the case in the VSM, where the same effect can only be obtained by both changing the weighting scheme and similarity measure at the expense of a more costly computation. Experiments are also reported to demonstrate and support the ideas, and they show that categoricity in HIR can be varied more than  O (  n ) faster, where  n  is the number of index terms, than in the VSM.},
journal = {Inf. Retr.},
month = apr,
pages = {265–283},
numpages = {19},
keywords = {similarity measures, ranking order preservation, hyperbolic geometry, information search and retrieval, Euclidean geometry, Cayley-Klein model}
}

@article{10.1007/s10791-005-5661-0,
author = {Lempel, R. and Moran, S.},
title = {Rank-Stability and Rank-Similarity of Link-Based Web Ranking Algorithms in Authority-Connected Graphs},
year = {2005},
issue_date = {April     2005},
publisher = {Kluwer Academic Publishers},
address = {USA},
volume = {8},
number = {2},
issn = {1386-4564},
url = {https://doi.org/10.1007/s10791-005-5661-0},
doi = {10.1007/s10791-005-5661-0},
abstract = {Web search algorithms that rank Web pages by examining the link structure of the Web are attractive from both theoretical and practical aspects. Today's prevailing link-based ranking algorithms rank Web pages by using the dominant eigenvector of certain matrices--like the co-citation matrix or variations thereof. Recent analyses of ranking algorithms have focused attention on the case where the corresponding matrices are irreducible, thus avoiding singularities of reducible matrices. Consequently, rank analysis has been concentrated on  authority connected  graphs, which are graphs whose co-citation matrix is irreducible (after deleting zero rows and columns). Such graphs conceptually correspond to thematically related collections, in which most pages pertain to a single, dominant topic of interest.A link-based search algorithm  A  is  rank-stable  if minor changes in the link structure of the input graph, which is usually a subgraph of the Web, do not affect the ranking it produces; algorithms  A ,  B  are  rank-similar  if they produce similar rankings. These concepts were introduced and studied recently for various existing search algorithms.This paper studies the rank-stability and rank-similarity of three link-based ranking algorithms--PageRank, HITS and SALSA--in authority connected graphs. For this class of graphs, we show that neither HITS nor PageRank is rank stable. We then show that HITS and PageRank are not rank similar on this class, nor is any of them rank similar to SALSA.},
journal = {Inf. Retr.},
month = apr,
pages = {245–264},
numpages = {20},
keywords = {link analysis, Web IR, citation}
}

@article{10.1007/s10791-005-5660-1,
author = {Agosti, Maristella and Pretto, Luca},
title = {A Theoretical Study of a Generalized Version of Kleinberg's HITS Algorithm},
year = {2005},
issue_date = {April     2005},
publisher = {Kluwer Academic Publishers},
address = {USA},
volume = {8},
number = {2},
issn = {1386-4564},
url = {https://doi.org/10.1007/s10791-005-5660-1},
doi = {10.1007/s10791-005-5660-1},
abstract = {Kleinberg's HITS algorithm (Kleinberg 1999), which was originally developed in a Web context, tries to infer the authoritativeness of a Web page in relation to a specific query using the structure of a subgraph of the Web graph, which is obtained considering this specific query. Recent applications of this algorithm in contexts far removed from that of Web searching (Bacchin, Ferro and Melucci 2002, Ng et al. 2001) inspired us to study the algorithm in the abstract, independently of its particular applications, trying to mathematically illuminate its behaviour. In the present paper we detail this theoretical analysis. The original work starts from the definition of a revised and more general version of the algorithm, which includes the classic one as a particular case. We perform an analysis of the structure of two particular matrices, essential to studying the behaviour of the algorithm, and we prove the convergence of the algorithm in the most general case, finding the analytic expression of the vectors to which it converges. Then we study the symmetry of the algorithm and prove the equivalence between the existence of symmetry and the independence from the order of execution of some basic operations on initial vectors. Finally, we expound some interesting consequences of our theoretical results.},
journal = {Inf. Retr.},
month = apr,
pages = {219–243},
numpages = {25},
keywords = {ranking, World Wide Web retrieval, link analysis, Kleinberg's HITS algorithm}
}

@article{10.1007/s10791-005-5659-7,
author = {Plachouras, Vassilis and Ounis, Iadh},
title = {Dempster-Shafer Theory for a Query-Biased Combination of Evidence on the Web},
year = {2005},
issue_date = {April     2005},
publisher = {Kluwer Academic Publishers},
address = {USA},
volume = {8},
number = {2},
issn = {1386-4564},
url = {https://doi.org/10.1007/s10791-005-5659-7},
doi = {10.1007/s10791-005-5659-7},
abstract = {This paper reports on a large-scale experiment for the evaluation of a formal query-biased combination of evidence mechanism. We use the Dempster-Shafer theory of evidence to combine optimally results obtained by content and link analyses on the Web. The query-biased mechanism is based on the query scope, a measure of the query specificity. The query scope is defined using a probabilistic propagation mechanism on top of the hierarchical structure of concepts provided by WordNet. We use two standard Web test collections and two different link analysis approaches. The results show that the proposed approach could improve the retrieval effectiveness.},
journal = {Inf. Retr.},
month = apr,
pages = {197–218},
numpages = {22},
keywords = {Dempster-Shafer theory of evidence, query-biased combination of evidence, query scope, combination of content and link analysis, Web information retrieval}
}

@article{10.1007/s10791-005-5658-8,
author = {Brants, Thorsten},
title = {Test Data Likelihood for PLSA Models},
year = {2005},
issue_date = {April     2005},
publisher = {Kluwer Academic Publishers},
address = {USA},
volume = {8},
number = {2},
issn = {1386-4564},
url = {https://doi.org/10.1007/s10791-005-5658-8},
doi = {10.1007/s10791-005-5658-8},
abstract = {Probabilistic Latent Semantic Analysis (PLSA) is a statistical latent class model that has recently received considerable attention. In its usual formulation it cannot assign likelihoods to unseen documents. Furthermore, it assigns a probability of zero to unseen documents during training. We point out that one of the two existing alternative formulations of the Expectation-Maximization algorithms for PLSA does not require this assumption. However, even that formulation does not allow calculation ofthe actual likelihood values. We therefore derive a new test-data likelihood substitute for PLSA and compare it to three existing likelihood substitutes. An empirical evaluation shows that our new likelihood substitute produces the best predictions about accuracies in two different IR tasks and is therefore best suited to determine the number of EM steps when training PLSA models. The new likelihood measure and its evaluation also suggest that PLSA is not very sensitive to overfitting for the two tasks considered. This renders additions like tempered EM that especially address overfitting unnecessary.},
journal = {Inf. Retr.},
month = apr,
pages = {181–196},
numpages = {16},
keywords = {likelihood, PLSA, Probabilistic Latent Semantic Analysis}
}

@article{10.1023/B:INRT.0000048496.31867.62,
author = {Xu, Yunjie and Benaroch, Michel},
title = {Information Retrieval with a Hybrid Automatic Query Expansion and Data Fusion Procedure},
year = {2005},
issue_date = {January 2005},
publisher = {Kluwer Academic Publishers},
address = {USA},
volume = {8},
number = {1},
issn = {1386-4564},
url = {https://doi.org/10.1023/B:INRT.0000048496.31867.62},
doi = {10.1023/B:INRT.0000048496.31867.62},
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.},
journal = {Inf. Retr.},
month = jan,
pages = {41–65},
numpages = {25},
keywords = {relevance-feedback, data fusion, multiple queries}
}

@article{10.1023/B:INRT.0000048495.64628.ea,
author = {Geffet, Maayan and Wiseman, Yair and Feitelson, Dror},
title = {Automatic Alphabet Recognition},
year = {2005},
issue_date = {January 2005},
publisher = {Kluwer Academic Publishers},
address = {USA},
volume = {8},
number = {1},
issn = {1386-4564},
url = {https://doi.org/10.1023/B:INRT.0000048495.64628.ea},
doi = {10.1023/B:INRT.0000048495.64628.ea},
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.},
journal = {Inf. Retr.},
month = jan,
pages = {25–40},
numpages = {16},
keywords = {letters' mapping, characters set, natural language alphabet}
}

@article{10.1023/B:INRT.0000048494.05013.6a,
author = {Spinellis, Diomidis},
title = {Index-Based Persistent Document Identifiers},
year = {2005},
issue_date = {January 2005},
publisher = {Kluwer Academic Publishers},
address = {USA},
volume = {8},
number = {1},
issn = {1386-4564},
url = {https://doi.org/10.1023/B:INRT.0000048494.05013.6a},
doi = {10.1023/B:INRT.0000048494.05013.6a},
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.},
journal = {Inf. Retr.},
month = jan,
pages = {5–24},
numpages = {20},
keywords = {search engine, distributed approach, index, persistency, URL}
}

@article{10.1023/B:INRT.0000048493.67375.93,
author = {Liggett, Walter and Buckley, Chris},
title = {System Performance and Natural Language Expression of Information Needs},
year = {2005},
issue_date = {January 2005},
publisher = {Kluwer Academic Publishers},
address = {USA},
volume = {8},
number = {1},
issn = {1386-4564},
url = {https://doi.org/10.1023/B:INRT.0000048493.67375.93},
doi = {10.1023/B:INRT.0000048493.67375.93},
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.},
journal = {Inf. Retr.},
month = jan,
pages = {101–128},
numpages = {28},
keywords = {query expansion, term weighting, research method, performance indicators, multidimensional scaling}
}

@article{10.1023/B:INRT.0000048492.50961.a6,
author = {Lemire, Daniel},
title = {Scale and Translation Invariant Collaborative Filtering Systems},
year = {2005},
issue_date = {January 2005},
publisher = {Kluwer Academic Publishers},
address = {USA},
volume = {8},
number = {1},
issn = {1386-4564},
url = {https://doi.org/10.1023/B:INRT.0000048492.50961.a6},
doi = {10.1023/B:INRT.0000048492.50961.a6},
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.},
journal = {Inf. Retr.},
month = jan,
pages = {129–150},
numpages = {22},
keywords = {recommender system, regression, e-commerce, incomplete vectors, energy minimization}
}

@article{10.1023/B:INRT.0000048491.59134.94,
author = {Bennett, Paul N. and Dumais, Susan T. and Horvitz, Eric},
title = {The Combination of Text Classifiers Using Reliability Indicators},
year = {2005},
issue_date = {January 2005},
publisher = {Kluwer Academic Publishers},
address = {USA},
volume = {8},
number = {1},
issn = {1386-4564},
url = {https://doi.org/10.1023/B:INRT.0000048491.59134.94},
doi = {10.1023/B:INRT.0000048491.59134.94},
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 indicators—variables 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.},
journal = {Inf. Retr.},
month = jan,
pages = {67–100},
numpages = {34},
keywords = {text classification, classifier combination, reliability indicators, feature selection, metaclassifiers}
}

@article{10.1023/B:INRT.0000048490.99518.5c,
author = {Anh, Vo Ngoc and Moffat, Alistair},
title = {Inverted Index Compression Using Word-Aligned Binary Codes},
year = {2005},
issue_date = {January 2005},
publisher = {Kluwer Academic Publishers},
address = {USA},
volume = {8},
number = {1},
issn = {1386-4564},
url = {https://doi.org/10.1023/B:INRT.0000048490.99518.5c},
doi = {10.1023/B:INRT.0000048490.99518.5c},
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.},
journal = {Inf. Retr.},
month = jan,
pages = {151–166},
numpages = {16},
keywords = {index representation, integer coding, index compression}
}

@article{10.1023/B:INRT.0000011242.39361.cb,
author = {Sebastiani, Fabrizio},
title = {Introduction: Special Issue on the 25th European Conference on Information Retrieval Research},
year = {2004},
issue_date = {September-December 2004},
publisher = {Kluwer Academic Publishers},
address = {USA},
volume = {7},
number = {3–4},
issn = {1386-4564},
url = {https://doi.org/10.1023/B:INRT.0000011242.39361.cb},
doi = {10.1023/B:INRT.0000011242.39361.cb},
journal = {Inf. Retr.},
month = sep,
pages = {235–237},
numpages = {3}
}

@article{10.1023/B:INRT.0000011212.66249.b7,
author = {Cheung, Kwok-Wai and Tian, Lily F.},
title = {Learning User Similarity and Rating Style for Collaborative Recommendation},
year = {2004},
issue_date = {September-December 2004},
publisher = {Kluwer Academic Publishers},
address = {USA},
volume = {7},
number = {3–4},
issn = {1386-4564},
url = {https://doi.org/10.1023/B:INRT.0000011212.66249.b7},
doi = {10.1023/B:INRT.0000011212.66249.b7},
abstract = {Information filtering is an area getting more important as we have long been flooded with too much information, where product brokering in e-commerce is a typical example. Systems which can provide personalized product recommendations to their users (often called recommender systems) have gained a lot of interest in recent years. Collaborative filtering is one of the commonly used approaches which normally requires a definition of user similarity measure. In the literature, researchers have proposed different choices for the similarity measure using different approaches, and yet there is no guarantee for optimality. In this paper, we propose the use of machine learning techniques to learn the optimal user similarity measure as well as user rating styles for enhancing recommendation acurracy. Based on a criterion function measuring the overall prediction error, several ratings transformation functions for modeling rating styles together with their learning algorithms are derived. With the help of the formulation and the optimization framework, subjective components in user ratings are removed so that the transformed ratings can then be compared. We have evaluated our proposed methods using the EachMovie dataset and succeeded in obtaining significant improvement in recommendation accuracy when compared with the standard correlation-based algorithm.},
journal = {Inf. Retr.},
month = sep,
pages = {395–410},
numpages = {16},
keywords = {collaborative filtering, machine learning, rating style, recommender systems, user similarity}
}

@article{10.1023/B:INRT.0000011211.50590.71,
author = {Lin, Raz and Kraus, Sarit and Tew, Jeffrey},
title = {OSGS—A Personalized Online Store for E-Commerce Environments},
year = {2004},
issue_date = {September-December 2004},
publisher = {Kluwer Academic Publishers},
address = {USA},
volume = {7},
number = {3–4},
issn = {1386-4564},
url = {https://doi.org/10.1023/B:INRT.0000011211.50590.71},
doi = {10.1023/B:INRT.0000011211.50590.71},
abstract = {Current online stores suffer from a cardinal problem. There are too many products to offer, and customers find themselves lost due to the vast selection. As opposed to traditional stores, there is little or no guidance that helps the customers as they search. In this paper, we propose a new approach for designing a successful personalized online store enabling the successful searching of customers in the store. This approach is based on algorithms commonly used in recommendation systems, but which are rarely used for searches in online stores. We employ this approach for both keyword and browse searches, and present an implementation of this approach. We compared several search guide algorithms experimentally, and the experiments' results show that the suggested algorithms are applicable to the domain of online stores.},
journal = {Inf. Retr.},
month = sep,
pages = {369–394},
numpages = {26},
keywords = {browse search, information retrieval, collaborative filtering, E-commerce, keyword search}
}

@article{10.1023/B:INRT.0000011210.12953.86,
author = {Makkonen, Juha and Ahonen-Myka, Helena and Salmenkivi, Marko},
title = {Simple Semantics in Topic Detection and Tracking},
year = {2004},
issue_date = {September-December 2004},
publisher = {Kluwer Academic Publishers},
address = {USA},
volume = {7},
number = {3–4},
issn = {1386-4564},
url = {https://doi.org/10.1023/B:INRT.0000011210.12953.86},
doi = {10.1023/B:INRT.0000011210.12953.86},
abstract = {Topic Detection and Tracking (TDT) is a research initiative that aims at techniques to organize news documents in terms of news events. We propose a method that incorporates simple semantics into TDT by splitting the term space into groups of terms that have the meaning of the same type. Such a group can be associated with an external ontology. This ontology is used to determine the similarity of two terms in the given group. We extract proper names, locations, temporal expressions and normal terms into distinct sub-vectors of the document representation. Measuring the similarity of two documents is conducted by comparing a pair of their corresponding sub-vectors at a time. We use a simple perceptron to optimize the relative emphasis of each semantic class in the tracking and detection decisions. The results suggest that the spatial and the temporal similarity measures need to be improved. Especially the vagueness of spatial and temporal terms needs to be addressed.},
journal = {Inf. Retr.},
month = sep,
pages = {347–368},
numpages = {22},
keywords = {topic detection and tracking, temporal expression, geographical ontology, retrieval model, information extraction}
}

@article{10.1023/B:INRT.0000011209.19643.e2,
author = {Peng, Fuchun and Schuurmans, Dale and Wang, Shaojun},
title = {Augmenting Naive Bayes Classifiers with Statistical Language Models},
year = {2004},
issue_date = {September-December 2004},
publisher = {Kluwer Academic Publishers},
address = {USA},
volume = {7},
number = {3–4},
issn = {1386-4564},
url = {https://doi.org/10.1023/B:INRT.0000011209.19643.e2},
doi = {10.1023/B:INRT.0000011209.19643.e2},
abstract = {We augment naive Bayes models with statistical n-gram language models to address short-comings of the standard naive Bayes text classifier. The result is a generalized naive Bayes classifier which allows for a local Markov dependence among observations; a model we refer to as the Chain Augmented Naive Bayes (CAN) Bayes classifier. CAN models have two advantages over standard naive Bayes classifiers. First, they relax some of the independence assumptions of naive Bayes—allowing a local Markov chain dependence in the observed variables—while still permitting efficient inference and learning. Second, they permit straightforward application of sophisticated smoothing techniques from statistical language modeling, which allows one to obtain better parameter estimates than the standard Laplace smoothing used in naive Bayes classification. In this paper, we introduce CAN models and apply them to various text classification problems. To demonstrate the language independent and task independent nature of these classifiers, we present experimental results on several text classification problems—authorship attribution, text genre classification, and topic detection—in several languages—Greek, English, Japanese and Chinese. We then systematically study the key factors in the CAN model that can influence the classification performance, and analyze the strengths and weaknesses of the model.},
journal = {Inf. Retr.},
month = sep,
pages = {317–345},
numpages = {29},
keywords = {text classification, n-gram language models, smoothing, naive Bayes}
}

@article{10.1023/B:INRT.0000011208.60754.a1,
author = {Braschler, Martin and Ripplinger, B\"{a}rbel},
title = {How Effective is Stemming and Decompounding for German Text Retrieval?},
year = {2004},
issue_date = {September-December 2004},
publisher = {Kluwer Academic Publishers},
address = {USA},
volume = {7},
number = {3–4},
issn = {1386-4564},
url = {https://doi.org/10.1023/B:INRT.0000011208.60754.a1},
doi = {10.1023/B:INRT.0000011208.60754.a1},
abstract = {Information retrieval systems operating on free text face difficulties when word forms used in the query and documents do not match. The usual solution is the use of a “stemming component” that reduces related word forms to a common stem. Extensive studies of such components exist for English, but considerably less is known for other languages. Previously, it has been claimed that stemming is essential for highly declensional languages. We report on our experiments on stemming for German, where an additional issue is the handling of compounds, which are formed by concatenating several words. The major contribution of our work that goes beyond its focus on German lies in the investigation of a complete spectrum of approaches, ranging from language-independent to elaborate linguistic methods. The main findings are that stemming is beneficial even when using a simple approach, and that carefully designed decompounding, the splitting of compound words, remarkably boosts performance. All findings are based on a thorough analysis using a large reliable test collection.},
journal = {Inf. Retr.},
month = sep,
pages = {291–316},
numpages = {26},
keywords = {morphological analysis, German, evaluation, stemming, decompounding}
}

@article{10.1023/B:INRT.0000011207.45988.bb,
author = {Harper, David J. and Koychev, Ivan and Sun, Yixing and Pirie, Iain},
title = {Within-Document Retrieval: A User-Centred Evaluation of Relevance Profiling},
year = {2004},
issue_date = {September-December 2004},
publisher = {Kluwer Academic Publishers},
address = {USA},
volume = {7},
number = {3–4},
issn = {1386-4564},
url = {https://doi.org/10.1023/B:INRT.0000011207.45988.bb},
doi = {10.1023/B:INRT.0000011207.45988.bb},
abstract = {We present a user-centred, task-oriented, comparative evaluation of two within-document retrieval tools. ProfileSkim computes a relevance profile for a document with respect to a query, and presents the profile as an interactive bar graph. FindSkim provides similar functionality to the web browser “Find” command. A novel simulated work task was devised, where participants are asked to identify (index) relevant pages of an electronic book, given topics from the existing book index. The original book index provides the ground truth, against which the indexing results of the participants can be compared. We confirmed a major hypothesis, namely ProfileSkim proved significantly more efficient than Find-Skim, as measured by time for task. The study indicates that ProfileSkim was as least as effective as FindSkim in identifying relevant pages, as measured by traditional information retrieval measures, and there is some evidence that ProfileSkim is a precision-enhancing tool. Based on qualitative data from questionnaires, we also provide strong evidence to support our conjecture that the participants would be more satisfied when using ProfileSkim than FindSkim. The experimental study confirmed the potential of relevance profiling for improving within-document retrieval. Relevance profiling should prove highly beneficial for users trying to identify relevant information within long documents.},
journal = {Inf. Retr.},
month = sep,
pages = {265–290},
numpages = {26},
keywords = {task-oriented evaluation, relevance profiling, interactive information retrieval, language models, within-document retrieval}
}

@article{10.1023/B:INRT.0000011206.23588.ab,
author = {Gurrin, Cathal and Smeaton, Alan F.},
title = {Replicating Web Structure in Small-Scale Test Collections},
year = {2004},
issue_date = {September-December 2004},
publisher = {Kluwer Academic Publishers},
address = {USA},
volume = {7},
number = {3–4},
issn = {1386-4564},
url = {https://doi.org/10.1023/B:INRT.0000011206.23588.ab},
doi = {10.1023/B:INRT.0000011206.23588.ab},
abstract = {Linkage analysis as an aid to web search has been assumed to be of significant benefit and we know that it is being implemented by many major Search Engines. Why then have few TREC participants been able to scientifically prove the benefits of linkage analysis in recent years? In this paper we put forward reasons why many disappointing results have been found in TREC experiments and we identify the linkage density requirements of a dataset to faithfully support experiments into linkage-based retrieval by examining the linkage structure of the WWW. Based on these requirements we report on methodologies for synthesising such a test collection.},
journal = {Inf. Retr.},
month = sep,
pages = {239–263},
numpages = {25},
keywords = {test collections, retrieval evaluation, linkage analysis, search engine}
}

@article{10.1023/B:INRT.0000009472.59294.f6,
author = {Peters, Carol and Braschler, Martin},
title = {Editorial},
year = {2004},
issue_date = {January-April 2004},
publisher = {Kluwer Academic Publishers},
address = {USA},
volume = {7},
number = {1–2},
issn = {1386-4564},
url = {https://doi.org/10.1023/B:INRT.0000009472.59294.f6},
doi = {10.1023/B:INRT.0000009472.59294.f6},
journal = {Inf. Retr.},
month = jan,
pages = {5},
numpages = {1}
}

@article{10.1023/B:INRT.0000009446.22036.e3,
author = {Oard, Douglas W. and Gonzalo, Julio and Sanderson, Mark and L\'{o}pez-Ostenero, Fernando and Wang, Jianqiang},
title = {Interactive Cross-Language Document Selection},
year = {2004},
issue_date = {January-April 2004},
publisher = {Kluwer Academic Publishers},
address = {USA},
volume = {7},
number = {1–2},
issn = {1386-4564},
url = {https://doi.org/10.1023/B:INRT.0000009446.22036.e3},
doi = {10.1023/B:INRT.0000009446.22036.e3},
abstract = {The problem of finding documents written in a language that the searcher cannot read is perhaps the most challenging application of cross-language information retrieval technology. In interactive applications, that task involves at least two steps: (1) the machine locates promising documents in a collection that is larger than the searcher could scan, and (2) the searcher recognizes documents relevant to their intended use from among those nominated by the machine. This article presents the results of experiments designed to explore three techniques for supporting interactive relevance assessment: (1) full machine translation, (2) rapid term-by-term translation, and (3) focused phrase translation. Machine translation was found to better support this task than term-by-term translation, and focused phrase translation further improved recall without an adverse effect on precision. The article concludes with an assessment of the strengths and weaknesses of the evaluation framework used in this study and some remarks on implications of these results for future evaluation campaigns.},
journal = {Inf. Retr.},
month = jan,
pages = {205–228},
numpages = {24},
keywords = {cross-language information retrieval, interactive information retrieval, evaluation of information retrieval systems, machine translation}
}

@article{10.1023/B:INRT.0000009445.19495.46,
author = {Braschler, Martin},
title = {Combination Approaches for Multilingual Text Retrieval},
year = {2004},
issue_date = {January-April 2004},
publisher = {Kluwer Academic Publishers},
address = {USA},
volume = {7},
number = {1–2},
issn = {1386-4564},
url = {https://doi.org/10.1023/B:INRT.0000009445.19495.46},
doi = {10.1023/B:INRT.0000009445.19495.46},
abstract = {We describe the Eurospider component for Cross-Language Information Retrieval (CLIR) that has been employed for experiments at all three CLEF campaigns to date. The central aspect of our efforts is the use of combination approaches, effectively combining multiple language pairs, translation resources and translation methods into one multilingual retrieval system. We discuss the implications of building a system that allows flexible combination, give details of the various translation resources and methods, and investigate the impact of merging intermediate results generated by the individual steps. An analysis of the resulting combination system is given which also takes into account additional requirements when deploying the system as a component in an operational, commercial setting.},
journal = {Inf. Retr.},
month = jan,
pages = {183–204},
numpages = {22},
keywords = {operational systems, combination methods, Cross-Language Information Retrieval, merging}
}

@article{10.1023/B:INRT.0000009444.89549.90,
author = {Chen, Aitao and Gey, Fredric C.},
title = {Multilingual Information Retrieval Using Machine Translation, Relevance Feedback and Decompounding},
year = {2004},
issue_date = {January-April 2004},
publisher = {Kluwer Academic Publishers},
address = {USA},
volume = {7},
number = {1–2},
issn = {1386-4564},
url = {https://doi.org/10.1023/B:INRT.0000009444.89549.90},
doi = {10.1023/B:INRT.0000009444.89549.90},
abstract = {Multilingual retrieval (querying of multiple document collections each in a different language) can be achieved by combining several individual techniques which enhance retrieval: machine translation to cross the language barrier, relevance feedback to add words to the initial query, decompounding for languages with complex term structure, and data fusion to combine monolingual retrieval results from different languages. Using the CLEF 2001 and CLEF 2002 topics and document collections, this paper evaluates these techniques within the context of a monolingual document ranking formula based upon logistic regression. Each individual technique yields improved performance over runs which do not utilize that technique. Moreover the techniques are complementary, in that combining the best techniques outperforms individual technique performance. An approximate but fast document translation using bilingual wordlists created from machine translation systems is presented and evaluated. The fast document translation is as effective as query translation in multilingual retrieval. Furthermore, when fast document translation is combined with query translation in multilingual retrieval, the performance is significantly better than that of query translation or fast document translation.},
journal = {Inf. Retr.},
month = jan,
pages = {149–182},
numpages = {34},
keywords = {multilingual information retrieval, results merging, cross-language information retrieval, relevance feedback, decompounding}
}

@article{10.1023/B:INRT.0000009443.51912.e7,
author = {Savoy, Jacques},
title = {Combining Multiple Strategies for Effective Monolingual and Cross-Language Retrieval},
year = {2004},
issue_date = {January-April 2004},
publisher = {Kluwer Academic Publishers},
address = {USA},
volume = {7},
number = {1–2},
issn = {1386-4564},
url = {https://doi.org/10.1023/B:INRT.0000009443.51912.e7},
doi = {10.1023/B:INRT.0000009443.51912.e7},
abstract = {This paper describes and evaluates different retrieval strategies that are useful for search operations on document collections written in various European languages, namely French, Italian, Spanish and German. We also suggest and evaluate different query translation schemes based on freely available translation resources. In order to cross language barriers, we propose a combined query translation approach that has resulted in interesting retrieval effectiveness. Finally, we suggest a collection merging strategy based on logistic regression that tends to perform better than other merging approaches.},
journal = {Inf. Retr.},
month = jan,
pages = {121–148},
numpages = {28},
keywords = {learning curve, cross-language information retrieval, collection merging strategies, bilingual information retrieval, evaluation}
}

@article{10.1023/B:INRT.0000009442.34054.55,
author = {Hedlund, Turid and Airio, Eija and Keskustalo, Heikki and Lehtokangas, Raija and Pirkola, Ari and J\"{a}rvelin, Kalervo},
title = {Dictionary-Based Cross-Language Information Retrieval: Learning Experiences from CLEF 2000–2002},
year = {2004},
issue_date = {January-April 2004},
publisher = {Kluwer Academic Publishers},
address = {USA},
volume = {7},
number = {1–2},
issn = {1386-4564},
url = {https://doi.org/10.1023/B:INRT.0000009442.34054.55},
doi = {10.1023/B:INRT.0000009442.34054.55},
abstract = {In this study the basic framework and performance analysis results are presented for the three year long development process of the dictionary-based UTACLIR system. The tests expand from bilingual CLIR for three language pairs Swedish, Finnish and German to English, to six language pairs, from English to French, German, Spanish, Italian, Dutch and Finnish, and from bilingual to multilingual. In addition, transitive translation tests are reported. The development process of the UTACLIR query translation system will be regarded from the point of view of a learning process. The contribution of the individual components, the effectiveness of compound handling, proper name matching and structuring of queries are analyzed. The results and the fault analysis have been valuable in the development process. Overall the results indicate that the process is robust and can be extended to other languages. The individual effects of the different components are in general positive. However, performance also depends on the topic set and the number of compounds and proper names in the topic, and to some extent on the source and target language. The dictionaries used affect the performance significantly.},
journal = {Inf. Retr.},
month = jan,
pages = {99–119},
numpages = {21},
keywords = {transitive CLIR, compound handling, proper name matching, UTACLIR query translation system, cross-language information retrieval}
}

@article{10.1023/B:INRT.0000009441.78971.be,
author = {Mcnamee, Paul and Mayfield, James},
title = {Character <i>N</i>-Gram Tokenization for European Language Text Retrieval},
year = {2004},
issue_date = {January-April 2004},
publisher = {Kluwer Academic Publishers},
address = {USA},
volume = {7},
number = {1–2},
issn = {1386-4564},
url = {https://doi.org/10.1023/B:INRT.0000009441.78971.be},
doi = {10.1023/B:INRT.0000009441.78971.be},
abstract = {The Cross-Language Evaluation Forum has encouraged research in text retrieval methods for numerous European languages and has developed durable test suites that allow language-specific techniques to be investigated and compared. The labor associated with crafting a retrieval system that takes advantage of sophisticated linguistic methods is daunting. We examine whether language-neutral methods can achieve accuracy comparable to language-specific methods with less concomitant software complexity. Using the CLEF 2002 test set we demonstrate empirically how overlapping character n-gram tokenization can provide retrieval accuracy that rivals the best current language-specific approaches for European languages. We show that n = 4 is a good choice for those languages, and document the increased storage and time requirements of the technique. We report on the benefits of and challenges posed by n-grams, and explain peculiarities attendant to bilingual retrieval. Our findings demonstrate clearly that accuracy using n-gram indexing rivals or exceeds accuracy using unnormalized words, for both monolingual and bilingual retrieval.},
journal = {Inf. Retr.},
month = jan,
pages = {73–97},
numpages = {25},
keywords = {European languages, language-neutral retrieval, character n-grams, Cross Language Evaluation Forum, cross-language information retrieval}
}

@article{10.1023/B:INRT.0000009440.64411.ad,
author = {Bertoldi, Nicola and Federico, Marcello},
title = {Statistical Models for Monolingual and Bilingual Information Retrieval},
year = {2004},
issue_date = {January-April 2004},
publisher = {Kluwer Academic Publishers},
address = {USA},
volume = {7},
number = {1–2},
issn = {1386-4564},
url = {https://doi.org/10.1023/B:INRT.0000009440.64411.ad},
doi = {10.1023/B:INRT.0000009440.64411.ad},
abstract = {This work reviews information retrieval systems developed at ITC-irst which were evaluated through several tracks of CLEF, during the last three years. The presentation tries to follow the progress made over time in developing new statistical models first for monolingual information retrieval, then for cross-language information retrieval. Besides describing the underlying theory, performance of monolingual and bilingual information retrieval models are reported, respectively, on Italian monolingual tracks and Italian-English bilingual tracks of CLEF. Monolingual systems by ITC-irst performed consistently well in all the official evaluations, while the bilingual system ranked in CLEF 2002 just behind competitors using commercial machine translation engines. However, by experimentally comparing our statistical topic translation model against a state-of-the-art commercial system, no statistically significant difference in retrieval performance could be measured on a larger set of queries.},
journal = {Inf. Retr.},
month = jan,
pages = {53–72},
numpages = {20},
keywords = {monolingual information retrieval, machine translation, statistical models, cross-language information retrieval}
}

@article{10.1023/B:INRT.0000009439.19151.4c,
author = {Hollink, Vera and Kamps, Jaap and Monz, Christof and De Rijke, Maarten},
title = {Monolingual Document Retrieval for European Languages},
year = {2004},
issue_date = {January-April 2004},
publisher = {Kluwer Academic Publishers},
address = {USA},
volume = {7},
number = {1–2},
issn = {1386-4564},
url = {https://doi.org/10.1023/B:INRT.0000009439.19151.4c},
doi = {10.1023/B:INRT.0000009439.19151.4c},
abstract = {Recent years have witnessed considerable advances in information retrieval for European languages other than English. We give an overview of commonly used techniques and we analyze them with respect to their impact on retrieval effectiveness. The techniques considered range from linguistically motivated techniques, such as morphological normalization and compound splitting, to knowledge-free approaches, such as n-gram indexing. Evaluations are carried out against data from the CLEF campaign, covering eight European languages. Our results show that for many of these languages a modicum of linguistic techniques may lead to improvements in retrieval effectiveness, as can the use of language independent techniques.},
journal = {Inf. Retr.},
month = jan,
pages = {33–52},
numpages = {20},
keywords = {tokenization, morphological normalization, cross-lingual information retrieval, monolingual document retrieval, European languages}
}

@article{10.1023/B:INRT.0000009438.69013.fa,
author = {Braschler, Martin and Peters, Carol},
title = {Cross-Language Evaluation Forum: Objectives, Results, Achievements},
year = {2004},
issue_date = {January-April 2004},
publisher = {Kluwer Academic Publishers},
address = {USA},
volume = {7},
number = {1–2},
issn = {1386-4564},
url = {https://doi.org/10.1023/B:INRT.0000009438.69013.fa},
doi = {10.1023/B:INRT.0000009438.69013.fa},
abstract = {The Cross-Language Evaluation Forum (CLEF) is now in its fourth year of activity. We summarize the main lessons learned during this period, outline the state-of-the-art of the research reported in the CLEF experiments and discuss the contribution that this initiative has made to research and development in the multilingual information access domain. We also make proposals for future directions in system evaluation aimed at meeting emerging needs.},
journal = {Inf. Retr.},
month = jan,
pages = {7–31},
numpages = {25},
keywords = {CLIR state-of-the-art, evaluation campaign, evaluation methodology, cross-language information retrieval}
}

@article{10.1023/A:1026080230789,
author = {Nottelmann, Henrik and Fuhr, Norbert},
title = {From Retrieval Status Values to Probabilities of Relevance for Advanced IR Applications},
year = {2003},
issue_date = {September-December 2003},
publisher = {Kluwer Academic Publishers},
address = {USA},
volume = {6},
number = {3–4},
issn = {1386-4564},
url = {https://doi.org/10.1023/A:1026080230789},
doi = {10.1023/A:1026080230789},
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.},
journal = {Inf. Retr.},
month = sep,
pages = {363–388},
numpages = {26},
keywords = {evaluation, formal models, parameter learning, retrieval status value, probability of inference}
}

@article{10.1023/A:1026028229881,
author = {Huang, Xiangji and Peng, Fuchun and Schuurmans, Dale and Cercone, Nick and Robertson, Stephen E.},
title = {Applying Machine Learning to Text Segmentation for Information Retrieval},
year = {2003},
issue_date = {September-December 2003},
publisher = {Kluwer Academic Publishers},
address = {USA},
volume = {6},
number = {3–4},
issn = {1386-4564},
url = {https://doi.org/10.1023/A:1026028229881},
doi = {10.1023/A:1026028229881},
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.},
journal = {Inf. Retr.},
month = sep,
pages = {333–362},
numpages = {30},
keywords = {Chinese information retrieval, machine learning, EM algorithm, word segmentation}
}

@article{10.1023/A:1026024513043,
author = {Wang, Quan and Ng, Yiu-Kai},
title = {An Ontology-Based Binary-Categorization Approach for Recognizing Multiple-Record Web Documents Using a Probabilistic Retrieval Model},
year = {2003},
issue_date = {September-December 2003},
publisher = {Kluwer Academic Publishers},
address = {USA},
volume = {6},
number = {3–4},
issn = {1386-4564},
url = {https://doi.org/10.1023/A:1026024513043},
doi = {10.1023/A:1026024513043},
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%.},
journal = {Inf. Retr.},
month = sep,
pages = {295–332},
numpages = {38},
keywords = {logistic regression analysis, application ontology, probabilistic model, binary categorization}
}

@article{10.1023/A:1023992406935,
author = {Katopol, Patricia},
title = {Knowledge Management in the Socio Technical World—The Graffiti Continues},
year = {2003},
issue_date = {April 2003},
publisher = {Kluwer Academic Publishers},
address = {USA},
volume = {6},
number = {2},
issn = {1386-4564},
url = {https://doi.org/10.1023/A:1023992406935},
doi = {10.1023/A:1023992406935},
journal = {Inf. Retr.},
month = apr,
pages = {281–284},
numpages = {4}
}

@article{10.1023/A:1023988306026,
author = {Sisson, Scott},
title = {Principles of Data Mining},
year = {2003},
issue_date = {April 2003},
publisher = {Kluwer Academic Publishers},
address = {USA},
volume = {6},
number = {2},
issn = {1386-4564},
url = {https://doi.org/10.1023/A:1023988306026},
doi = {10.1023/A:1023988306026},
journal = {Inf. Retr.},
month = apr,
pages = {275–277},
numpages = {3}
}

@article{10.1023/A:1023984205118,
author = {Guo, David and Berry, Michael W. and Thompson, Bryan B. and Bailin, Sidney},
title = {Knowledge-Enhanced Latent Semantic Indexing},
year = {2003},
issue_date = {April 2003},
publisher = {Kluwer Academic Publishers},
address = {USA},
volume = {6},
number = {2},
issn = {1386-4564},
url = {https://doi.org/10.1023/A:1023984205118},
doi = {10.1023/A:1023984205118},
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.},
journal = {Inf. Retr.},
month = apr,
pages = {225–250},
numpages = {26},
keywords = {latent semantic indexing, MeSH, metathesaurus, OHSUMED, semantic network, UMLS}
}

@article{10.1023/A:1023947204209,
author = {Lu, Zhihong and McKinley, Kathryn S.},
title = {Partial Collection Replication for Information Retrieval},
year = {2003},
issue_date = {April 2003},
publisher = {Kluwer Academic Publishers},
address = {USA},
volume = {6},
number = {2},
issn = {1386-4564},
url = {https://doi.org/10.1023/A:1023947204209},
doi = {10.1023/A:1023947204209},
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.},
journal = {Inf. Retr.},
month = apr,
pages = {159–198},
numpages = {40},
keywords = {replica selection, partial replication, distributed information retrieval architectures}
}

@article{10.1023/A:1023944523773,
author = {Solomon, Paul},
title = {Looking for Information—A Survey of Research on Information Seeking, Needs, and Behavior},
year = {2003},
issue_date = {April 2003},
publisher = {Kluwer Academic Publishers},
address = {USA},
volume = {6},
number = {2},
issn = {1386-4564},
url = {https://doi.org/10.1023/A:1023944523773},
doi = {10.1023/A:1023944523773},
journal = {Inf. Retr.},
month = apr,
pages = {284–288},
numpages = {5}
}

@article{10.1023/A:1023940422865,
author = {Wacholder, Nina},
title = {Spotting and Discovering Terms Through Natural Language Processing},
year = {2003},
issue_date = {April 2003},
publisher = {Kluwer Academic Publishers},
address = {USA},
volume = {6},
number = {2},
issn = {1386-4564},
url = {https://doi.org/10.1023/A:1023940422865},
doi = {10.1023/A:1023940422865},
journal = {Inf. Retr.},
month = apr,
pages = {277–281},
numpages = {5}
}

@article{10.1023/A:1023936321956,
author = {Vechtomova, Olga and Robertson, Stephen and Jones, Susan},
title = {Query Expansion with Long-Span Collocates},
year = {2003},
issue_date = {April 2003},
publisher = {Kluwer Academic Publishers},
address = {USA},
volume = {6},
number = {2},
issn = {1386-4564},
url = {https://doi.org/10.1023/A:1023936321956},
doi = {10.1023/A:1023936321956},
abstract = {The paper presents two novel approaches to query expansion with long-span collocates—words, significantly co-occurring in topic-size windows with query terms. In the first approach—global collocation analysis—collocates of query terms are extracted from the entire collection, in the second—local collocation analysis—from 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.},
journal = {Inf. Retr.},
month = apr,
pages = {251–273},
numpages = {23},
keywords = {automatic query expansion, relevance feedback, long-span word collocation, probabilistic information retrieval}
}

@article{10.1023/A:1023932221048,
author = {Mostafa, Javed and Mukhopadhyay, Snehasis and Palakal, Mathew},
title = {Simulation Studies of Different Dimensions of Users' Interests and Their Impact on User Modeling and Information Filtering},
year = {2003},
issue_date = {April 2003},
publisher = {Kluwer Academic Publishers},
address = {USA},
volume = {6},
number = {2},
issn = {1386-4564},
url = {https://doi.org/10.1023/A:1023932221048},
doi = {10.1023/A:1023932221048},
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.},
journal = {Inf. Retr.},
month = apr,
pages = {199–223},
numpages = {25},
keywords = {information filtering, user modeling, reinforcement learning, user-oriented factors}
}

@article{10.1023/A:1022952531694,
author = {Elovici, Yuval and Shapira, Bracha and Kantor, Paul B.},
title = {Using the Information Structure Model to Compare Profile-Based Information Filtering Systems},
year = {2003},
issue_date = {January 2003},
publisher = {Kluwer Academic Publishers},
address = {USA},
volume = {6},
number = {1},
issn = {1386-4564},
url = {https://doi.org/10.1023/A:1022952531694},
doi = {10.1023/A:1022952531694},
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.},
journal = {Inf. Retr.},
month = jan,
pages = {75–97},
numpages = {23},
keywords = {user-profile, information economics, performance, evaluation, formal model}
}

@article{10.1023/A:1022949613039,
author = {Trotman, Andrew},
title = {Compressing Inverted Files},
year = {2003},
issue_date = {January 2003},
publisher = {Kluwer Academic Publishers},
address = {USA},
volume = {6},
number = {1},
issn = {1386-4564},
url = {https://doi.org/10.1023/A:1022949613039},
doi = {10.1023/A:1022949613039},
abstract = {Research into inverted file compression has focused on compression ratio—how 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.},
journal = {Inf. Retr.},
month = jan,
pages = {5–19},
numpages = {15},
keywords = {document indexing, index compression, inverted files, text searching}
}

@article{10.1023/A:1022948414856,
author = {Sakkis, Georgios and Androutsopoulos, Ion and Paliouras, Georgios and Karkaletsis, Vangelis and Spyropoulos, Constantine D. and Stamatopoulos, Panagiotis},
title = {A Memory-Based Approach to Anti-Spam Filtering for Mailing Lists},
year = {2003},
issue_date = {January 2003},
publisher = {Kluwer Academic Publishers},
address = {USA},
volume = {6},
number = {1},
issn = {1386-4564},
url = {https://doi.org/10.1023/A:1022948414856},
doi = {10.1023/A:1022948414856},
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.},
journal = {Inf. Retr.},
month = jan,
pages = {49–73},
numpages = {25},
keywords = {text categorization, unsolicited commercial e-mail, machine learning, spam}
}

@article{10.1023/A:1022944313947,
author = {Van Der Pol, Ruud},
title = {Dipe-D: A Tool for Knowledge-Based Query Formulation in Information Retrieval},
year = {2003},
issue_date = {January 2003},
publisher = {Kluwer Academic Publishers},
address = {USA},
volume = {6},
number = {1},
issn = {1386-4564},
url = {https://doi.org/10.1023/A:1022944313947},
doi = {10.1023/A:1022944313947},
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 area—as represented in a knowledge base—is 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.},
journal = {Inf. Retr.},
month = jan,
pages = {21–47},
numpages = {27},
keywords = {information retrieval, knowledge engineering/artificial intelligence, query formulation}
}

@article{10.1023/A:1022904715765,
author = {Hawking, David and Robertson, Stephen},
title = {On Collection Size and Retrieval Effectiveness},
year = {2003},
issue_date = {January 2003},
publisher = {Kluwer Academic Publishers},
address = {USA},
volume = {6},
number = {1},
issn = {1386-4564},
url = {https://doi.org/10.1023/A:1022904715765},
doi = {10.1023

license_type: permissive

branch_name: refs/heads/main

extension: bib