text:
/A:1022904715765},
abstract = {The relationship between collection size and retrieval effectiveness is particularly important in the context of Web search. We investigate it first analytically and then experimentally, using samples and subsets of test collections. Different retrieval systems vary in how the score assigned to an individual document in a sample collection relates to the score it receives in the full collection; we identify four cases.We apply signal detection (SD) theory to retrieval from samples, taking into account the four cases and using a variety of shapes for relevant and irrelevant distributions. We note that the SD model subsumes several earlier hypotheses about the causes of the decreased precision in samples. We also discuss other models which contribute to an understanding of the phenomenon, particularly relating to the effects of discreteness. Different models provide complementary insights.Extensive use is made of test data, some from official submissions to the TREC-6 VLC track and some new, to illustrate the effects and test hypotheses. We empirically confirm predictions, based on SD theory, that P@n should decline when moving to a sample collection and that average precision and R-precision should remain constant. SD theory suggests the use of recall-fallout plots as operating characteristic (OC) curves. We plot OC curves of this type for a real retrieval system and query set and show that curves for sample collections are similar but not identical to the curve for the full collection.},
journal = {Inf. Retr.},
month = jan,
pages = {99–105},
numpages = {7},
keywords = {text retrieval models, relevance score distributions, collection sampling, signal detection theory}
}

@article{10.1023/A:1020499411651,
author = {Bookstein, Abraham and Kulyukin, Vladimir A. and Raita, Timo},
title = {Generalized Hamming Distance},
year = {2002},
issue_date = {October 2002},
publisher = {Kluwer Academic Publishers},
address = {USA},
volume = {5},
number = {4},
issn = {1386-4564},
url = {https://doi.org/10.1023/A:1020499411651},
doi = {10.1023/A:1020499411651},
abstract = {Many problems in information retrieval and related fields depend on a reliable measure of the distance or similarity between objects that, most frequently, are represented as vectors. This paper considers vectors of bits. Such data structures implement entities as diverse as bitmaps that indicate the occurrences of terms and bitstrings indicating the presence of edges in images. For such applications, a popular distance measure is the Hamming distance. The value of the Hamming distance for information retrieval applications is limited by the fact that it counts only exact matches, whereas in information retrieval, corresponding bits that are close by can still be considered to be almost identical. We define a “Generalized Hamming distance” that extends the Hamming concept to give partial credit for near misses, and suggest a dynamic programming algorithm that permits it to be computed efficiently. We envision many uses for such a measure. In this paper we define and prove some basic properties of the “Generalized Hamming distance”, and illustrate its use in the area of object recognition. We evaluate our implementation in a series of experiments, using autonomous robots to test the measure's effectiveness in relating similar bitstrings.},
journal = {Inf. Retr.},
month = oct,
pages = {353–375},
numpages = {23},
keywords = {image retrieval, metrics, object recognition, hamming distance, robot vision, information retrieval, computer vision}
}

@article{10.1023/A:1020447427581,
author = {French, James C. and Powell, Allison L. and Gey, Fredric and Perelman, Natalia},
title = {Exploiting Manual Indexing to Improve Collection Selection and Retrieval Effectiveness},
year = {2002},
issue_date = {October 2002},
publisher = {Kluwer Academic Publishers},
address = {USA},
volume = {5},
number = {4},
issn = {1386-4564},
url = {https://doi.org/10.1023/A:1020447427581},
doi = {10.1023/A:1020447427581},
abstract = {Vocabulary incompatibilities arise when the terms used to index a document collection are largely unknown, or at least not well-known to the users who eventually search the collection. No matter how comprehensive or well-structured the indexing vocabulary, it is of little use if it is not used effectively in query formulation. This paper demonstrates that techniques for mapping user queries into the controlled indexing vocabulary have the potential to radically improve document retrieval performance. We also show how the use of controlled indexing vocabulary can be employed to achieve performance gains for collection selection. Finally, we demonstrate the potential benefit of combining these two techniques in an interactive retrieval environment. Given a user query, our evaluation approach simulates the human user's choice of terms for query augmentation given a list of controlled vocabulary terms suggested by a system. This strategy lets us evaluate interactive strategies without the need for human subjects.},
journal = {Inf. Retr.},
month = oct,
pages = {323–351},
numpages = {29},
keywords = {collection selection, query augmentation, entry vocabulary, interactive retrieval}
}

@article{10.1023/A:1020443909834,
author = {Herlocker, Jon and Konstan, Joseph A. and Riedl, John},
title = {An Empirical Analysis of Design Choices in Neighborhood-Based Collaborative Filtering Algorithms},
year = {2002},
issue_date = {October 2002},
publisher = {Kluwer Academic Publishers},
address = {USA},
volume = {5},
number = {4},
issn = {1386-4564},
url = {https://doi.org/10.1023/A:1020443909834},
doi = {10.1023/A:1020443909834},
abstract = {Collaborative filtering systems predict a user's interest in new items based on the recommendations of other people with similar interests. Instead of performing content indexing or content analysis, collaborative filtering systems rely entirely on interest ratings from members of a participating community. Since predictions are based on human ratings, collaborative filtering systems have the potential to provide filtering based on complex attributes, such as quality, taste, or aesthetics. Many implementations of collaborative filtering apply some variation of the neighborhood-based prediction algorithm. Many variations of similarity metrics, weighting approaches, combination measures, and rating normalization have appeared in each implementation. For these parameters and others, there is no consensus as to which choice of technique is most appropriate for what situations, nor how significant an effect on accuracy each parameter has. Consequently, every person implementing a collaborative filtering system must make hard design choices with little guidance. This article provides a set of recommendations to guide design of neighborhood-based prediction systems, based on the results of an empirical study. We apply an analysis framework that divides the neighborhood-based prediction approach into three components and then examines variants of the key parameters in each component. The three components identified are similarity computation, neighbor selection, and rating combination.},
journal = {Inf. Retr.},
month = oct,
pages = {287–310},
numpages = {24},
keywords = {information filtering, collaborative filtering, preference prediction, empirical studies}
}

@article{10.1023/A:1020443310743,
author = {Nilsson, Martin},
title = {Hierarchical Clustering Using Non-Greedy Principal Direction Divisive Partitioning},
year = {2002},
issue_date = {October 2002},
publisher = {Kluwer Academic Publishers},
address = {USA},
volume = {5},
number = {4},
issn = {1386-4564},
url = {https://doi.org/10.1023/A:1020443310743},
doi = {10.1023/A:1020443310743},
abstract = {We present a non-greedy version of the recently published Principal Direction Divisive Partitioning (PDDP) algorithm. The PDDP algorithm creates a hierarchical taxonomy of a data set by successively splitting the data into sub-clusters. At each level the cluster with largest variance is split by a hyper-plane orthogonal to its leading principal component. The PDDP algorithm is known to produce high quality clusters, especially when applied to high dimensional data, such as document-word feature matrices. It also scales well with both the size and the dimensionality of the data set. However, at each level only the locally optimal choice of spitting is considered. At a later stage this often leads to a non-optimal global partitioning of the data. The non-greedy version of the PDDP algorithm (NGPDDP) presented in this paper address this problem. At each level multiple alternative splitting strategies are considered. Results from applying the algorithm to generated and real data (feature vectors from sets of text documents) are presented. The results show substantial improvements in the cluster quality.},
journal = {Inf. Retr.},
month = oct,
pages = {311–321},
numpages = {11},
keywords = {PCA, taxonomy, classification, clustering}
}

@article{10.1023/A:1015797928606,
author = {Soboroff, Ian M. and Nicholas, Charles K.},
title = {Related, but Not Relevant: Content-Based Collaborative Filtering in TREC-8},
year = {2002},
issue_date = {April-July 2002},
publisher = {Kluwer Academic Publishers},
address = {USA},
volume = {5},
number = {2–3},
issn = {1386-4564},
url = {https://doi.org/10.1023/A:1015797928606},
doi = {10.1023/A:1015797928606},
abstract = {Historically, solutions to the TREC filtering tasks have focused exclusively on the content of documents and search topic descriptions as training data. These approaches are well-known for their ability to focus on those salient concepts in the document stream which are most useful for separating relevant documents from irrelevant ones. However, one kind of information that has not been used is the relationships among the topics themselves. In our TREC-8 routing experiments, we employed a collaborative (or social) filtering algorithm, based on latent semantic indexing which highlights common term usage patterns among groups of filtering profiles. Our hypothesis was that this would allow related topics to share common relevant documents. We found, however, that the algorithm also recommends many documents of related, yet irrelevant interest. As a result of this process, many similar search topics are “linked” together by common sets of documents recommended to them. We visualize these topic relationships using graphs where topics are nodes and edges exist where two topics share a recommended document.},
journal = {Inf. Retr.},
month = apr,
pages = {189–208},
numpages = {20},
keywords = {content-based filtering, collaborative filtering, routing, profile learning}
}

@article{10.1023/A:1015793827697,
author = {Allan, James},
title = {Detection As Multi-Topic Tracking},
year = {2002},
issue_date = {April-July 2002},
publisher = {Kluwer Academic Publishers},
address = {USA},
volume = {5},
number = {2–3},
issn = {1386-4564},
url = {https://doi.org/10.1023/A:1015793827697},
doi = {10.1023/A:1015793827697},
abstract = {The topic tracking task from TDT is a variant of information filtering tasks that focuses on event-based topics in streams of broadcast news. In this study, we compare tracking to another TDT task, detection, which has the goal of partitioning i>all arriving news into topics, regardless of whether the topics are of interest to anyone, and even when a new topic appears that had not been previous anticipated. There are clear relationships between the two tasks (under some assumptions, a “perfect” tracking system could “solve” the detection problem), but they are evaluated quite differently. We describe the two tasks and discuss their similarities. We show how viewing detection as a form of multi-topic parallel tracking can illuminate the performance tradeoffs of detection over tracking.},
journal = {Inf. Retr.},
month = apr,
pages = {139–157},
numpages = {19},
keywords = {event-based information organization, evaluation, topic detection and tracking (TDT), information filtering}
}

@article{10.1023/A:1015758214493,
author = {Zaragoza, Hugo},
title = {Information Retrieval: Algorithms and Heuristics},
year = {2002},
issue_date = {April-July 2002},
publisher = {Kluwer Academic Publishers},
address = {USA},
volume = {5},
number = {2–3},
issn = {1386-4564},
url = {https://doi.org/10.1023/A:1015758214493},
doi = {10.1023/A:1015758214493},
journal = {Inf. Retr.},
month = apr,
pages = {271–274},
numpages = {4}
}

@article{10.1023/A:1015754113585,
author = {Robertson, Stephen},
title = {Comparing the Performance of Adaptive Filtering and Ranked Output Systems},
year = {2002},
issue_date = {April-July 2002},
publisher = {Kluwer Academic Publishers},
address = {USA},
volume = {5},
number = {2–3},
issn = {1386-4564},
url = {https://doi.org/10.1023/A:1015754113585},
doi = {10.1023/A:1015754113585},
abstract = {Some insight into the behavior of adaptive filtering systems may be gained by comparing them with similar ranked-output retrieval systems. This is not easy; however, a new optimization measure, introduced for the TREC-9 filtering track, makes some such comparison possible. A series of experiments using the TREC-9 filtering data shows that filtering effectiveness is comparable to routing effectiveness, and demonstrates the gains to be made from adaptation.},
journal = {Inf. Retr.},
month = apr,
pages = {257–268},
numpages = {12},
keywords = {filtering, ranked output, performance}
}

@article{10.1023/A:1015750012676,
author = {Eichmann, David and Srinivasan, Padmini},
title = {Adaptive Filtering of Newswire Stories Using Two-Level Clustering},
year = {2002},
issue_date = {April-July 2002},
publisher = {Kluwer Academic Publishers},
address = {USA},
volume = {5},
number = {2–3},
issn = {1386-4564},
url = {https://doi.org/10.1023/A:1015750012676},
doi = {10.1023/A:1015750012676},
abstract = {Adaptive filtering of news is an area of information retrieval gaining substantial interest as services become more available on the Internet. This paper reports on a number of experiments involving a two-level clustering approach using a variety of techniques including threshold adaptation, topic vocabulary adaptation and both noun phrase and named entity adaptation. Our goal in this exploratory research is to empirically compare alternative configurations of our filtering approach that will allow us to better understand the relative value of the component subsystems.},
journal = {Inf. Retr.},
month = apr,
pages = {209–237},
numpages = {29},
keywords = {document clustering, part-of-speech tagging, adaptive filtering, named entity extraction}
}

@article{10.1023/A:1015745911767,
author = {Ault, Thomas Galen and Yang, Yiming},
title = {Information Filtering in TREC-9 and TDT-3: A Comparative Analysis},
year = {2002},
issue_date = {April-July 2002},
publisher = {Kluwer Academic Publishers},
address = {USA},
volume = {5},
number = {2–3},
issn = {1386-4564},
url = {https://doi.org/10.1023/A:1015745911767},
doi = {10.1023/A:1015745911767},
abstract = {Much work on automated information filtering has been done in the TREC and TDT domains, but differences in corpora, the nature of TREC topics vs. TDT events, the constraints imposed on training and testing, and the choices of performance measures confound any meaningful comparison between these domains. We attempt to bridge the gap between them by evaluating the performance of the k-nearest-neighbor (kNN) classification system on the corpus and categories from one domain using the constraints of the other. To maximize comparability and understand the effect of the evaluation metrics specific to each domain, we optimize the performance of kNN separately for the i>F1, i>T9P (preferred metric for TREC-9) and i>Ctrk (official metric for TDT-3) metrics. Through a thorough comparison of our within-domain and cross-domain results, our results demonstrate that the corpus used for TREC-9 is more challenging for an information filtering system than the TDT-3 corpus and strongly suggest that the TDT-3 event tracking task itself is more difficult than the TREC batch filtering task. We also show that optimizing performance in TREC-9 and TDT-3 tends to result in systems with different performance characteristics, confounding any meaningful comparison between the two domains, and that i>T9P and i>Ctrk both have properties that make them undesirable as general information filtering metrics.},
journal = {Inf. Retr.},
month = apr,
pages = {159–187},
numpages = {29},
keywords = {information filtering, TREC, TDT, topic tracking}
}

@article{10.1023/A:1015721010859,
author = {Robertson, Stephen},
title = {Introduction to the Special Issue: Overview of the TREC Routing and Filtering Tasks},
year = {2002},
issue_date = {April-July 2002},
publisher = {Kluwer Academic Publishers},
address = {USA},
volume = {5},
number = {2–3},
issn = {1386-4564},
url = {https://doi.org/10.1023/A:1015721010859},
doi = {10.1023/A:1015721010859},
abstract = {This paper introduces the special issue, and reviews the routing and filtering tasks as defined and evaluated at TREC. The tasks attempt to simulate a specific service situation: the system is assumed to process an incoming stream of documents against profiles of user interest, strictly in the time order in which they arrive, and immediately refer any matching document to the user. In the adaptive filtering version of the task, the user is assumed to provide a relevance judgement instantly. The rationale for the task definitions and the evaluation measures used is discussed.},
journal = {Inf. Retr.},
month = apr,
pages = {127–137},
numpages = {11},
keywords = {routing, filtering, TREC}
}

@article{10.1023/A:1015710331332,
author = {Holbrooks, M. Zoe},
title = {The Text in the Machine: Electronic Texts in the Humanities},
year = {2002},
issue_date = {April-July 2002},
publisher = {Kluwer Academic Publishers},
address = {USA},
volume = {5},
number = {2–3},
issn = {1386-4564},
url = {https://doi.org/10.1023/A:1015710331332},
doi = {10.1023/A:1015710331332},
journal = {Inf. Retr.},
month = apr,
pages = {274–278},
numpages = {5}
}

@article{10.1023/A:1015706230423,
author = {Thompson, Paul},
title = {Finding Out About: A Cognitive Perspective on Search Engine Technology and the WWW},
year = {2002},
issue_date = {April-July 2002},
publisher = {Kluwer Academic Publishers},
address = {USA},
volume = {5},
number = {2–3},
issn = {1386-4564},
url = {https://doi.org/10.1023/A:1015706230423},
doi = {10.1023/A:1015706230423},
journal = {Inf. Retr.},
month = apr,
pages = {269–271},
numpages = {3}
}

@article{10.1023/A:1015702129514,
author = {Robertson, Stephen},
title = {Threshold Setting and Performance Optimization in Adaptive Filtering},
year = {2002},
issue_date = {April-July 2002},
publisher = {Kluwer Academic Publishers},
address = {USA},
volume = {5},
number = {2–3},
issn = {1386-4564},
url = {https://doi.org/10.1023/A:1015702129514},
doi = {10.1023/A:1015702129514},
abstract = {An experimental adaptive filtering system, built on the Okapi search engine, is described. In addition to the regular text retrieval functions, the system requires a complex set of procedures for setting score thresholds and adapting them following feedback. These procedures need to be closely related to the evaluation measures to be used. A mixture of quantitative methods relating a threshold to the number of documents expected to be retrieved in a time period, and qualitative methods relating to the probability of relevance, is defined. Experiments under the TREC-9 Adaptive Filtering Track rules are reported. The system is seen to perform reasonably well in comparison with other systems at TREC. Some of the variables that may affect performance are investigated.},
journal = {Inf. Retr.},
month = apr,
pages = {239–256},
numpages = {18},
keywords = {optimization, adaptation, filtering, thresholds}
}

@article{10.1023/A:1012782908347,
author = {Ruiz, Miguel E. and Srinivasan, Padmini},
title = {Hierarchical Text Categorization Using Neural Networks},
year = {2002},
issue_date = {January 2002},
publisher = {Kluwer Academic Publishers},
address = {USA},
volume = {5},
number = {1},
issn = {1386-4564},
url = {https://doi.org/10.1023/A:1012782908347},
doi = {10.1023/A:1012782908347},
abstract = {This paper presents the design and evaluation of a text categorization method based on the Hierarchical Mixture of Experts model. This model uses a divide and conquer principle to define smaller categorization problems based on a predefined hierarchical structure. The final classifier is a hierarchical array of neural networks. The method is evaluated using the UMLS Metathesaurus as the underlying hierarchical structure, and the OHSUMED test set of MEDLINE records. Comparisons with an optimized version of the traditional Rocchio's algorithm adapted for text categorization, as well as flat neural network classifiers are provided. The results show that the use of the hierarchical structure improves text categorization performance with respect to an equivalent flat model. The optimized Rocchio algorithm achieves a performance comparable with that of the hierarchical neural networks.},
journal = {Inf. Retr.},
month = jan,
pages = {87–118},
numpages = {32},
keywords = {automatic text categorization, applied neural networks, hierarchical classifiers}
}

@article{10.1023/A:1012778807438,
author = {Kuriyama, Kazuko and Kando, Noriko and Nozue, Toshihiko and Eguchi, Koji},
title = {Pooling for a Large-Scale Test Collection: An Analysis of the Search Results from the First NTCIR Workshop},
year = {2002},
issue_date = {January 2002},
publisher = {Kluwer Academic Publishers},
address = {USA},
volume = {5},
number = {1},
issn = {1386-4564},
url = {https://doi.org/10.1023/A:1012778807438},
doi = {10.1023/A:1012778807438},
abstract = {We have conducted a study to: (1) verify the exhaustiveness of pooling for the purpose of constructing a large-scale test collection, and (2) examine whether a difference in the number of pool documents can affect the relative evaluation of IR systems. We carried out the experiments using search topics, their relevance assessments, and the search results that were submitted for both the pre-test and test of the first NTCIR Workshop.Our results verified the efficiency and the effectiveness of the pooling method, the exhaustiveness of the relevance assessments, and the reliability of the evaluation using the test collection based on the pooling method.},
journal = {Inf. Retr.},
month = jan,
pages = {41–59},
numpages = {19},
keywords = {exhaustiveness of pooling, test collection, evaluation methodology, IR system, relevance assessment}
}

@article{10.1023/A:1012730924277,
author = {Chen, Zhixiang and Zhu, Binhai},
title = {Some Formal Analysis of Rocchio's Similarity-Based Relevance Feedback Algorithm},
year = {2002},
issue_date = {January 2002},
publisher = {Kluwer Academic Publishers},
address = {USA},
volume = {5},
number = {1},
issn = {1386-4564},
url = {https://doi.org/10.1023/A:1012730924277},
doi = {10.1023/A:1012730924277},
abstract = {Rocchio's similarity-based Relevance feedback algorithm, one of the most important query reformation methods in information retrieval, is essentially an adaptive supervised learning algorithm from examples. In spite of its popularity in various applications there is little rigorous analysis of its learning complexity in literature. In this paper we show that in the binary vector space model, if the initial query vector is  0 , then for any of the four typical similarities (inner product, dice coefficient, cosine coefficient, and Jaccard coefficient), Rocchio's similarity-based relevance feedback algorithm makes at least i>n mistakes when used to search for a collection of documents represented by a monotone disjunction of at most i>k relevant features (or terms) over the i>n-dimensional binary vector space {0, 1}i>n. When an arbitrary initial query vector in {0, 1}i>n is used, it makes at least (i>n + i>k \'{y} 3)/2 mistakes to search for the same collection of documents. The linear lower bounds are independent of the choices of the threshold and coefficients that the algorithm may use in updating its query vector and making its classification.},
journal = {Inf. Retr.},
month = jan,
pages = {61–86},
numpages = {26},
keywords = {supervised learning, vector space, lower bound, relevance feedback, similarity}
}

@article{10.1023/A:1012714523368,
author = {Ko\l{}cz, Aleksander and Alspector, Joshua},
title = {Asymmetric Missing-Data Problems: Overcoming the Lack of Negative Data in Preference Ranking},
year = {2002},
issue_date = {January 2002},
publisher = {Kluwer Academic Publishers},
address = {USA},
volume = {5},
number = {1},
issn = {1386-4564},
url = {https://doi.org/10.1023/A:1012714523368},
doi = {10.1023/A:1012714523368},
abstract = {In certain classification problems there is a strong a asymmetry between the number of labeled examples available for each of the classes involved. In an extreme case, there may be a complete lack of labeled data for one of the classes while, at the same time, there are adequate labeled examples for the others, accompanied by a large body of unlabeled data. Since most classification algorithms require some information about all classes involved, label estimation for the un-represented class is desired. An important representative of this group of problems is that of user interest/preference modeling where there may be a large number of examples of what the user likes with essentially no counterexamples.Recently, there has been much interest in applying the EM algorithm to incomplete data problems in the area of text retrieval and categorization. We adapt this approach to the asymmetric case of modeling user interests in news articles, where only labeled positive training data are available, with access to a large corpus of unlabeled documents. User modeling is here equivalent to that of user-specific document ranking. EM is used in conjunction with the Naive Bayes model while its output is also utilized by a Support Vector Machine and Rocchio's technique.Our findings demonstrate that the EM algorithm can be quite effective in modeling the negative class under a number of different initialization schemes. Although primarily just the negative training examples are needed, a natural question is whether using all of the estimated labels (i.e., positive and negative) would be more (or less) beneficial. This is important considering that, in this context, the initialization of the negative class for EM is likely not to be very accurate. Experimental results suggest that EM output should be limited to negative label estimates only.},
journal = {Inf. Retr.},
month = jan,
pages = {5–40},
numpages = {36},
keywords = {user modeling, incomplete data problems, imbalanced training data, personalization, information retrieval}
}

@article{10.5555/593964.594032,
author = {Vakkari, Pertti},
title = {Changes in Search Tactics and Relevance Judgements When Preparing a Research Proposal A Summary of the Findings of a Longitudinal Study},
year = {2001},
issue_date = {September-December 2001},
publisher = {Kluwer Academic Publishers},
address = {USA},
volume = {4},
number = {3–4},
issn = {1386-4564},
abstract = {The article summarizes the empirical results on relations between students' problem stages in the course of writing their research proposals for their master's theses and the information sought, choice of search terms and tactics and relevance assessments of the information found for that task. The study is based on Kuhlthau's model of the information search process. The results of the study show that there is a close connection between the students' problem stages (mental model) in the task performance and the information sought, search tactics used, and the assessment of the relevance and utility of the information found. The corroborated hypotheses extend and specify ideas in Kuhlthau's model in the domain of IR. A theory of task-based information searching based on the empirical findings of the study is presented.},
journal = {Inf. Retr.},
month = sep,
pages = {295–310},
numpages = {16},
keywords = {a theory of information retrieval, tasks, relevance, usefullness on information, search tactics}
}

@article{10.1023/A:1011998222190,
author = {J\"{a}rvelin, Kalervo and Kek\"{a}l\"{a}inen, Jaana and Niemi, Timo},
title = {ExpansionTool: Concept-Based Query Expansion and Construction},
year = {2001},
issue_date = {September-December 2001},
publisher = {Kluwer Academic Publishers},
address = {USA},
volume = {4},
number = {3–4},
issn = {1386-4564},
url = {https://doi.org/10.1023/A:1011998222190},
doi = {10.1023/A:1011998222190},
abstract = {We develop a deductive data model for concept-based query expansion. It is based on three abstraction levels: the conceptual, linguistic and string levels. Concepts and relationships among them are represented at the conceptual level. The linguistic level gives natural language expressions for concepts. Each expression has one or more matching patterns at the string level. The models specify the matching of the expression in database indices built in varying ways. The data model supports a declarative concept-based query expansion and formulation tool, the ExpansionTool, for heterogeneous IR system environments. Conceptual expansion is implemented by a novel intelligent operator for traversing transitive relationships among cyclic concept networks. The number of expansion links followed, their types, and weights can be used to control expansion. A sample empirical experiment illustrating the use of the ExpansionTool in IR experiments is presented.},
journal = {Inf. Retr.},
month = sep,
pages = {231–255},
numpages = {25},
keywords = {deductive data model, ontology, concept-based information retrieval, conceptual models, query formulation}
}

@article{10.1023/A:1011994105352,
author = {Pirkola, Ari and Hedlund, Turid and Keskustalo, Heikki and J\"{a}rvelin, Kalervo},
title = {Dictionary-Based Cross-Language Information Retrieval: Problems, Methods, and Research Findings},
year = {2001},
issue_date = {September-December 2001},
publisher = {Kluwer Academic Publishers},
address = {USA},
volume = {4},
number = {3–4},
issn = {1386-4564},
url = {https://doi.org/10.1023/A:1011994105352},
doi = {10.1023/A:1011994105352},
abstract = {This paper reviews literature on dictionary-based cross-language information retrieval (CLIR) and presents CLIR research done at the University of Tampere (UTA). The main problems associated with dictionary-based CLIR, as well as appropriate methods to deal with the problems are discussed. We will present the structured query model by Pirkola and report findings for four different language pairs concerning the effectiveness of query structuring. The architecture of our automatic query translation and construction system is presented.},
journal = {Inf. Retr.},
month = sep,
pages = {209–230},
numpages = {22},
keywords = {cross-language information retrieval, structured queries, dictionary-based translation}
}

@article{10.1023/A:1011980920373,
author = {Kek\"{a}l\"{a}inen, Jaana},
title = {Information Retrieval Special Issue: Conceptual, Linguistic and Task-Based IR: Research at the University of Tampere},
year = {2001},
issue_date = {September-December 2001},
publisher = {Kluwer Academic Publishers},
address = {USA},
volume = {4},
number = {3–4},
issn = {1386-4564},
url = {https://doi.org/10.1023/A:1011980920373},
doi = {10.1023/A:1011980920373},
journal = {Inf. Retr.},
month = sep,
pages = {191–194},
numpages = {4}
}

@article{10.1023/A:1011954407169,
author = {Markkula, Marjo and Tico, Marius and Sepponen, Bemmu and Nirkkonen, Katja and Sormunen, Eero},
title = {A Test Collection for the Evaluation of Content-Based Image Retrieval Algorithms—A User and Task-Based Approach},
year = {2001},
issue_date = {September-December 2001},
publisher = {Kluwer Academic Publishers},
address = {USA},
volume = {4},
number = {3–4},
issn = {1386-4564},
url = {https://doi.org/10.1023/A:1011954407169},
doi = {10.1023/A:1011954407169},
abstract = {Content-based image retrieval (CBIR) algorithms have been seen as a promising access method for digital photograph collections. Unfortunately, we have very little evidence of the usefulness of these algorithms in real user needs and contexts. In this paper, we introduce a test collection for the evaluation of CBIR algorithms. In the test collection, the performance testing is based on photograph similarity perceived by end-users in the context of realistic illustration tasks and environment. The building process and the characteristics of the resulting test collection are outlined, including a typology of similarity criteria expressed by the subjects judging the similarity of photographs. A small-scale study on the consistency of similarity assessments is presented. A case evaluation of two CBIR algorithms is reported. The results show clear correlation between the subjects' similarity assessments and the functioning of feature parameters of the tested algorithms.},
journal = {Inf. Retr.},
month = sep,
pages = {275–293},
numpages = {19},
keywords = {evaluation, test collections, content-based image retrieval, user needs}
}

@article{10.1023/A:1011950323099,
author = {Sormunen, Eero},
title = {Extensions to the STAIRS Study—Empirical Evidence for the Hypothesised Ineffectiveness of Boolean Queries in Large Full-Text Databases},
year = {2001},
issue_date = {September-December 2001},
publisher = {Kluwer Academic Publishers},
address = {USA},
volume = {4},
number = {3–4},
issn = {1386-4564},
url = {https://doi.org/10.1023/A:1011950323099},
doi = {10.1023/A:1011950323099},
abstract = {The STAIRS study conducted by Blair and Maron in the mid-80's is a milestone in the history of IR evaluation. Blair and Maron made strong conclusion about the inadequacy of free-text searching large databases, and their study has been widely referred in the literature to justify the problems of effectiveness in IR systems. However, some critics of the study have plausibly pointed out that the ineffectiveness conclusions were not solidly based on empirical data.This paper introduces a new theoretical and empirical approach to study the problems of high recall searching in large databases and reports the results of a case experiment. The findings verify some of the hypothetical conclusions introduced in the STAIRS study, and expands the picture of falling performance. It is shown that low precision in high recall searching is unavoidable in exact-match Boolean searching since even major concepts are often expressed implicitly in relevant documents. The author suggests that the problem could be reduced in facet-based best-match searching.},
journal = {Inf. Retr.},
month = sep,
pages = {257–273},
numpages = {17},
keywords = {STAIRS study, full-text databases, Boolean queries, IR evaluation}
}

@article{10.1023/A:1011942104443,
author = {Alkula, Riitta},
title = {From Plain Character Strings to Meaningful Words: Producing Better Full Text Databases for Inflectional and Compounding Languages with Morphological Analysis Software},
year = {2001},
issue_date = {September-December 2001},
publisher = {Kluwer Academic Publishers},
address = {USA},
volume = {4},
number = {3–4},
issn = {1386-4564},
url = {https://doi.org/10.1023/A:1011942104443},
doi = {10.1023/A:1011942104443},
abstract = {The paper deals with linguistic processing and retrieval techniques in fulltext databases. Special attention is focused on the characteristics of highly inflectional languages, and how morphological structure of a language should be taken into account, when designing and developing information retrieval systems. Finnish is used as an example of a language, which has a more complicated inflectional structure than the English language. In the FULLTEXT project, natural language analysis modules for Finnish were incorporated into the commercial BASIS information retrieval system, which is based on inverted files and Boolean searching. Several test databases were produced, each using one or two Finnish morphological analysis programs.},
journal = {Inf. Retr.},
month = sep,
pages = {195–208},
numpages = {14},
keywords = {natural language processing, full text retrieval, stemming, morphology}
}

@article{10.5555/593963.594025,
author = {Kantor, Paul B.},
title = {Book Reviews},
year = {2001},
issue_date = {July 2001},
publisher = {Kluwer Academic Publishers},
address = {USA},
volume = {4},
number = {2},
issn = {1386-4564},
journal = {Inf. Retr.},
month = jul,
pages = {175–181},
numpages = {7}
}

@article{10.1023/A:1011471129047,
author = {Lopresti, Daniel P.},
title = {A Comparison of Text-Based Methods for Detecting Duplication in Scanned Document Databases},
year = {2001},
issue_date = {July 2001},
publisher = {Kluwer Academic Publishers},
address = {USA},
volume = {4},
number = {2},
issn = {1386-4564},
url = {https://doi.org/10.1023/A:1011471129047},
doi = {10.1023/A:1011471129047},
abstract = {This paper presents an experimental evaluation of several text-based methods for detecting duplication in scanned document databases using uncorrected OCR output. This task is made challenging both by the wide range of degradations printed documents can suffer, and by conflicting interpretations of what it means to be a “duplicate.” We report results for four sets of experiments exploring various aspects of the problem space. While the techniques studied are generally robust in the face of most types of OCR errors, there are nonetheless important differences which we identify and discuss in detail.},
journal = {Inf. Retr.},
month = jul,
pages = {153–173},
numpages = {21},
keywords = {optical character recognition, duplicate detection, approximate string matching, information retrieval, document analysis}
}

@article{10.1023/A:1011466928139,
author = {Kim, Jee-Hyub and Kwak, Byung-Kwan and Lee, Seungwoo and Lee, Geunbae and Lee, Jong-Hyeok},
title = {A Corpus-Based Learning Method of Compound Noun Indexing Rules for Korean},
year = {2001},
issue_date = {July 2001},
publisher = {Kluwer Academic Publishers},
address = {USA},
volume = {4},
number = {2},
issn = {1386-4564},
url = {https://doi.org/10.1023/A:1011466928139},
doi = {10.1023/A:1011466928139},
abstract = {In Korean information retrieval, compound nouns play an important role in improving precision in search experiments. There are two major approaches to compound noun indexing in Korean: statistical and linguistic. Each method, however, has its own shortcomings, such as limitations when indexing diverse types of compound nouns, over-generation of compound nouns, and data sparseness in training. In this paper, we propose a corpus-based learning method, which can index diverse types of compound nouns using rules automatically extracted from a large corpus. The automatic learning method is more portable and requires less human effort, although it exhibits a performance level similar to the manual-linguistic approach. We also present a new filtering method to solve the problems of compound noun over-generation and data sparseness.},
journal = {Inf. Retr.},
month = jul,
pages = {115–132},
numpages = {18},
keywords = {corpus-based learning, information retrieval, filtering, compound noun indexing, search performance evaluation}
}

@article{10.1023/A:1011458711300,
author = {Benkhalifa, Mohammed and Mouradi, Abdelhak and Bouyakhf, Houssaine},
title = {Integrating External Knowledge to Supplement Training Data in Semi-Supervised Learning for Text Categorization},
year = {2001},
issue_date = {July 2001},
publisher = {Kluwer Academic Publishers},
address = {USA},
volume = {4},
number = {2},
issn = {1386-4564},
url = {https://doi.org/10.1023/A:1011458711300},
doi = {10.1023/A:1011458711300},
abstract = {Text Categorization (TC) is the automated assignment of text documents to predefined categories based on document contents. TC has been an application for many learning approaches, which prove effective. Nevertheless, TC provides many challenges to machine learning. In this paper, we suggest, for text categorization, the integration of external WordNet lexical information to supplement training data for a semi-supervised clustering algorithm which can learn from both training and test documents to classify new unseen documents. This algorithm is the “Semi-Supervised Fuzzy c-Means” (ssFCM). Our experiments use Reuters 21578 database and consist of binary classifications for categories selected from the 115 TOPICS classes of the Reuters collection. Using the Vector Space Model, each document is represented by its original feature vector augmented with external feature vector generated using WordNet. We verify experimentally that the integration of WordNet helps ssFCM improve its performance, effectively addresses the classification of documents into categories with few training documents and does not interfere with the use of training data.},
journal = {Inf. Retr.},
month = jul,
pages = {91–113},
numpages = {23},
keywords = {text categorization, vector space model, Reuters Database, WordNet lexical database, semi supervised Fuzzy c Means}
}

@article{10.1023/A:1011419012209,
author = {Goldberg, Ken and Roeder, Theresa and Gupta, Dhruv and Perkins, Chris},
title = {Eigentaste: A Constant Time Collaborative Filtering Algorithm},
year = {2001},
issue_date = {July 2001},
publisher = {Kluwer Academic Publishers},
address = {USA},
volume = {4},
number = {2},
issn = {1386-4564},
url = {https://doi.org/10.1023/A:1011419012209},
doi = {10.1023/A:1011419012209},
abstract = {Eigentaste is a collaborative filtering algorithm that uses i>universal queries to elicit real-valued user ratings on a common set of items and applies principal component analysis (PCA) to the resulting dense subset of the ratings matrix. PCA facilitates dimensionality reduction for offline clustering of users and rapid computation of recommendations. For a database of i>n users, standard nearest-neighbor techniques require i>O(i>n) processing time to compute recommendations, whereas Eigentaste requires i>O(1) (constant) time. We compare Eigentaste to alternative algorithms using data from i>Jester, an online joke recommending system.Jester has collected approximately 2,500,000 ratings from 57,000 users. We use the Normalized Mean Absolute Error (NMAE) measure to compare performance of different algorithms. In the Appendix we use Uniform and Normal distribution models to derive analytic estimates of NMAE when predictions are random. On the Jester dataset, Eigentaste computes recommendations two orders of magnitude faster with no loss of accuracy. Jester is online at: http://eigentaste.berkeley.edu},
journal = {Inf. Retr.},
month = jul,
pages = {133–151},
numpages = {19},
keywords = {collaborative filtering, recommender systems, dimensionality reduction, jokes}
}

@article{10.1023/A:1011476409104,
author = {Liddy, Elizabeth},
title = {Advances in Automatic Text Summarization},
year = {2001},
issue_date = {April 2001},
publisher = {Kluwer Academic Publishers},
address = {USA},
volume = {4},
number = {1},
issn = {1386-4564},
url = {https://doi.org/10.1023/A:1011476409104},
doi = {10.1023/A:1011476409104},
journal = {Inf. Retr.},
month = apr,
pages = {82–83},
numpages = {2}
}

@article{10.1023/A:1011472308196,
author = {Hersh, William},
title = {Managing Gigabytes—Compressing and Indexing Documents and Images (Second Edition)},
year = {2001},
issue_date = {April 2001},
publisher = {Kluwer Academic Publishers},
address = {USA},
volume = {4},
number = {1},
issn = {1386-4564},
url = {https://doi.org/10.1023/A:1011472308196},
doi = {10.1023/A:1011472308196},
journal = {Inf. Retr.},
month = apr,
pages = {79–80},
numpages = {2}
}

@article{10.1023/A:1011468107287,
author = {Hawking, David and Craswell, Nick and Bailey, Peter and Griffihs, Kathleen},
title = {Measuring Search Engine Quality},
year = {2001},
issue_date = {April 2001},
publisher = {Kluwer Academic Publishers},
address = {USA},
volume = {4},
number = {1},
issn = {1386-4564},
url = {https://doi.org/10.1023/A:1011468107287},
doi = {10.1023/A:1011468107287},
abstract = {The effectiveness of twenty public search engines is evaluated using TREC-inspired methods and a set of 54 queries taken from real Web search logs. The World Wide Web is taken as the test collection and a combination of crawler and text retrieval system is evaluated. The engines are compared on a range of measures derivable from binary relevance judgments of the first seven live results returned. Statistical testing reveals a significant difference between engines and high intercorrelations between measures. Surprisingly, given the dynamic nature of the Web and the time elapsed, there is also a high correlation between results of this study and a previous study by Gordon and Pathak. For nearly all engines, there is a gradual decline in precision at increasing cutoff after some initial fluctuation. Performance of the engines as a group is found to be inferior to the group of participants in the TREC-8 Large Web task, although the best engines approach the median of those systems. Shortcomings of current Web search evaluation methodology are identified and recommendations are made for future improvements. In particular, the present study and its predecessors deal with queries which are assumed to derive from a need to find a selection of documents relevant to a topic. By contrast, real Web search reflects a range of other information need types which require different judging and different measures.},
journal = {Inf. Retr.},
month = apr,
pages = {33–59},
numpages = {27},
keywords = {search engines, Web search, evaluation}
}

@article{10.1023/A:1011441423217,
author = {Zhang, Tong and J. Oles, Frank},
title = {Text Categorization Based on Regularized Linear Classification Methods},
year = {2001},
issue_date = {April 2001},
publisher = {Kluwer Academic Publishers},
address = {USA},
volume = {4},
number = {1},
issn = {1386-4564},
url = {https://doi.org/10.1023/A:1011441423217},
doi = {10.1023/A:1011441423217},
abstract = {A number of linear classification methods such as the linear least squares fit (LLSF), logistic regression, and support vector machines (SVM's) have been applied to text categorization problems. These methods share the similarity by finding hyperplanes that approximately separate a class of document vectors from its complement. However, support vector machines are so far considered special in that they have been demonstrated to achieve the state of the art performance. It is therefore worthwhile to understand whether such good performance is unique to the SVM design, or if it can also be achieved by other linear classification methods. In this paper, we compare a number of known linear classification methods as well as some variants in the framework of regularized linear systems. We will discuss the statistical and numerical properties of these algorithms, with a focus on text categorization. We will also provide some numerical experiments to illustrate these algorithms on a number of datasets.},
journal = {Inf. Retr.},
month = apr,
pages = {5–31},
numpages = {27},
keywords = {linear classification, regularization, text categorization}
}

@article{10.1023/A:1011424425034,
author = {Kantor, Paul},
title = {Foundations of Statistical Natural Language Processing},
year = {2001},
issue_date = {April 2001},
publisher = {Kluwer Academic Publishers},
address = {USA},
volume = {4},
number = {1},
issn = {1386-4564},
url = {https://doi.org/10.1023/A:1011424425034},
doi = {10.1023/A:1011424425034},
journal = {Inf. Retr.},
month = apr,
pages = {80–81},
numpages = {2}
}

@article{10.1023/A:1011420324125,
author = {Zhang, Jin},
title = {The Characteristic Analysis of the DARE Visual Space},
year = {2001},
issue_date = {April 2001},
publisher = {Kluwer Academic Publishers},
address = {USA},
volume = {4},
number = {1},
issn = {1386-4564},
url = {https://doi.org/10.1023/A:1011420324125},
doi = {10.1023/A:1011420324125},
abstract = {In this paper a distance-angle-based visual retrieval tool i>DARE is introduced. The distance-based similarity distribution, angle-based similarity distribution, and the differences of their distributions in the visual space are analyzed. The document cluster analysis in the visual space and the document cluster comparison between the document space and the visual space are addressed. A new conceptDistance to Reference Axisis introduced to better understand the visual space. The impact of other operations in DARE on the document distribution is discussed. Future research directions including significance of the index term distribution in the visual space and a user study are addressed.},
journal = {Inf. Retr.},
month = apr,
pages = {61–78},
numpages = {18},
keywords = {angle-base similarity measure, DARE, visualization for information retrieval, Visual space analysis, distance-based similarity measure}
}

@article{10.1023/A:1009986331526,
author = {Liddy, Elizabeth D. and Diamond, Ted and Mckenna, Mary},
title = {DR-LINK in TIPSTER III},
year = {2000},
issue_date = {December 2000},
publisher = {Kluwer Academic Publishers},
address = {USA},
volume = {3},
number = {4},
issn = {1386-4564},
url = {https://doi.org/10.1023/A:1009986331526},
doi = {10.1023/A:1009986331526},
abstract = {A Natural Language Processing based Information Retrieval System that was one of the original systems developed in Phase I of TIPSTER, was the basis of research in TIPSTER III the goal of which was to add two extended capabilities to the core system. Following a description of the multiple levels of linguistic processing that were developed for the original DR-LINK System, details are provided on research into query-specific data fusion and query-specific cross-document summarization. Experimental results show that there is potential for improving retrieval through query-specific fusion and that analysts found the Detailed Multiple Document Summary to be extremely useful for almost every query, while the Thumbnail sketch was useful in approximately 50% of the queries.},
journal = {Inf. Retr.},
month = dec,
pages = {291–311},
numpages = {21},
keywords = {natural language processing, automatic summarization, data fusion, information retrieval}
}

@article{10.1023/A:1009955715597,
author = {Kwok, K. L.},
title = {Improving English and Chinese Ad-Hoc Retrieval: A Tipster Text Phase 3 Project Report},
year = {2000},
issue_date = {December 2000},
publisher = {Kluwer Academic Publishers},
address = {USA},
volume = {3},
number = {4},
issn = {1386-4564},
url = {https://doi.org/10.1023/A:1009955715597},
doi = {10.1023/A:1009955715597},
abstract = {Both English and Chinese ad-hoc information retrieval were investigated in this Tipster 3 project. Part of our objectives is to study the use of various term level and phrasal level evidence to improve retrieval accuracy. For short queries, we studied five term level techniques that together can lead to good improvements over standard ad-hoc 2-stage retrieval for TREC5-8 experiments. For long queries, we studied the use of linguistic phrases to re-rank retrieval lists. Its effect is small but consistently positive.For Chinese IR, we investigated three simple representations for documents and queries: short-words, bigrams and characters. Both approximate short-word segmentation or bigrams, augmented with characters, give highly effective results. Accurate word segmentation appears not crucial for overall result of a query set. Character indexing by itself is not competitive. Additional improvements may be obtained using collection enrichment and combination of retrieval lists.Our PIRCS document-focused retrieval is also shown to have similarity with a simple language model approach to IR.},
journal = {Inf. Retr.},
month = dec,
pages = {313–338},
numpages = {26},
keywords = {language model and PIRCS retrieval model, pseudo-relevance feedback, ad-hoc two-stage retrieval, segmentation and Chinese IR, collection enrichment}
}

@article{10.1023/A:1009911900286,
author = {Yager, Ronald R.},
title = {A Hierarchical Document Retrieval Language},
year = {2000},
issue_date = {December 2000},
publisher = {Kluwer Academic Publishers},
address = {USA},
volume = {3},
number = {4},
issn = {1386-4564},
url = {https://doi.org/10.1023/A:1009911900286},
doi = {10.1023/A:1009911900286},
abstract = {The focus of this work is on the development of a document retrieval language which attempts to enable users to better represent their requirements with respect to retrieved documents. We describe a framework for evaluating documents which allows, in the spirit of computing with words, a linguistic specification of the interrelationship between the desired attributes. This framework, which makes considerable use of the Ordered Weighted Averaging (OWA) operator, also supports a hierarchical structure which allows for an increased expressiveness of queries.},
journal = {Inf. Retr.},
month = dec,
pages = {357–377},
numpages = {21},
keywords = {aggregation methods, hierarchical concepts, concept formation, information retrieval}
}

@article{10.1023/A:1009907816505,
author = {Davis, Mark W. and Ogden, William C.},
title = {Towards Universal Text Retrieval: Tipster Text Retrieval Research at New Mexico State University},
year = {2000},
issue_date = {December 2000},
publisher = {Kluwer Academic Publishers},
address = {USA},
volume = {3},
number = {4},
issn = {1386-4564},
url = {https://doi.org/10.1023/A:1009907816505},
doi = {10.1023/A:1009907816505},
abstract = {New Mexico State University's Computing Research Lab has participated in research in all three phases of the US Government's Tipster program. Our work on information retrieval has focused on research and development of multilingual and cross-language approaches to automatic retrieval. The work on automatic systems has been supplemented by additional research into the role of the IR system user in interactive retrieval scenarios: monolingual, multilingual and cross-language. The combined efforts suggest that “universal” text retrieval, in which a user can find, access and use documents in the face of language differences and information overload, may be possible.},
journal = {Inf. Retr.},
month = dec,
pages = {339–356},
numpages = {18},
keywords = {user studies, multilingual retrieval, unicode, interactive retrieval, cross-language retrieval}
}

@article{10.1023/A:1026572910743,
author = {Baumgarten, Christoph},
title = {Retrieving Information from a Distributed Heterogeneous Document Collection},
year = {2000},
issue_date = {October 2000},
publisher = {Kluwer Academic Publishers},
address = {USA},
volume = {3},
number = {3},
issn = {1386-4564},
url = {https://doi.org/10.1023/A:1026572910743},
doi = {10.1023/A:1026572910743},
abstract = {This paper describes a probabilistic model for optimum information retrieval in a distributed heterogeneous environment.The model assumes the collection of documents offered by the environment to be partitioned into subcollections. Documents as well as subcollections have to be indexed, where indexing methods using different indexing vocabularies can be employed. A query provided by a user is answered in terms of a ranked list of documents. The model determines a procedure for ranking the documents that stems from the Probability Ranking Principle: For each subcollection, the subcollection's documents are ranked; the resulting ranked lists are combined into a final ranked list of documents, where the ordering is determined by the documents' probabilities of being relevant with respect to the user's query. Various probabilistic ranking methods may be involved in the distributed ranking process. A criterion for effectively limiting the ranking process to a subset of subcollections extends the model.The property that different ranking methods and indexing vocabularies can be used is important when the subcollections are heterogeneous with respect to their content.The model's applicability is experimentally confirmed. When exploiting the degrees of freedom provided by the model, experiments showed evidence that the model even outperforms comparable models for the non-distributed case with respect to retrieval effectiveness.},
journal = {Inf. Retr.},
month = oct,
pages = {253–271},
numpages = {19},
keywords = {heterogeneity, distributed information retrieval, probability ranking principle}
}

@article{10.1023/A:1026568809834,
author = {Csillaghy, A. and Hinterberger, H. and Benz, A. O.},
title = {Content-Based Image Retrieval in Astronomy},
year = {2000},
issue_date = {October 2000},
publisher = {Kluwer Academic Publishers},
address = {USA},
volume = {3},
number = {3},
issn = {1386-4564},
url = {https://doi.org/10.1023/A:1026568809834},
doi = {10.1023/A:1026568809834},
abstract = {Content-based image retrieval in astronomy needs methods that can deal with an image content made of noisy and diffuse structures. This motivates investigations on how information should be summarized and indexed for this specific kind of images. The method we present first summarizes the image information content by partitioning the image in regions with same texture. We call this process texture summarization. Second, indexing features are generated by examining the distribution of parameters describing image regions. Indexing features can be associated with global or local image characteristics. Both kinds of indexing features are evaluated on the retrieval system of the Zurich archive of solar radio spectrograms. The evaluation shows that generating local indexing features using self-organizing maps yields the best effectiveness of all tested methods.},
journal = {Inf. Retr.},
month = oct,
pages = {229–241},
numpages = {13},
keywords = {image feature indexing, self-organizing maps, astronomy, image archives}
}

@article{10.1023/A:1026564708926,
author = {Mittendorf, Elke and Sch\"{a}uble, Peter},
title = {Information Retrieval Can Cope with Many Errors},
year = {2000},
issue_date = {October 2000},
publisher = {Kluwer Academic Publishers},
address = {USA},
volume = {3},
number = {3},
issn = {1386-4564},
url = {https://doi.org/10.1023/A:1026564708926},
doi = {10.1023/A:1026564708926},
abstract = {The retrieval of documents that originate from digitized and OCR-converted paper documents is an important task for modern retrieval systems. The problems that OCR errors cause for the retrieval process have been subject to research for several years now. We approach the problem from a theoretical point of view and model OCR conversion as a random experiment. Our theoretical results, which are supported by experiments, show clearly that information retrieval can cope even with many errors. It is, however, important that the documents are not too short and that recognition errors are distributed appropriately among words and documents. These results disclose that an expensive manual or automatic post-processing of OCR-converted documents usually does not make sense, but that scanning and OCR must be performed in an appropriate way and with care.},
journal = {Inf. Retr.},
month = oct,
pages = {189–216},
numpages = {28},
keywords = {optical character recognition, probabilistic modelling, data corruption, retrieval effectiveness}
}

@article{10.1023/A:1026560608017,
author = {Sch\"{a}uble, Peter and Mittendorf, Elke},
title = {Introduction},
year = {2000},
issue_date = {October 2000},
publisher = {Kluwer Academic Publishers},
address = {USA},
volume = {3},
number = {3},
issn = {1386-4564},
url = {https://doi.org/10.1023/A:1026560608017},
doi = {10.1023/A:1026560608017},
journal = {Inf. Retr.},
month = oct,
pages = {171–172},
numpages = {2}
}

@article{10.1023/A:1026525127581,
author = {Braschler, Martin and Sch\"{a}uble, Peter},
title = {Using Corpus-Based Approaches in a System for Multilingual Information Retrieval},
year = {2000},
issue_date = {October 2000},
publisher = {Kluwer Academic Publishers},
address = {USA},
volume = {3},
number = {3},
issn = {1386-4564},
url = {https://doi.org/10.1023/A:1026525127581},
doi = {10.1023/A:1026525127581},
abstract = {We present a system for multilingual information retrieval that allows users to formulate queries in their preferred language and retrieve relevant information from a collection containing documents in multiple languages. The system is based on a process of document level alignments, where documents of different languages are paired according to their similarity. The resulting mapping allows us to produce a multilingual comparable corpus. Such a corpus has multiple interesting applications. It allows us to build a data structure for query translation in cross-language information retrieval (CLIR). Moreover, we also perform pseudo relevance feedback on the alignments to improve our retrieval results. And finally, multiple retrieval runs can be merged into one unified result list. The resulting system is inexpensive, adaptable to domain-specific collections and new languages and has performed very well at the TREC-7 conference CLIR system comparison.},
journal = {Inf. Retr.},
month = oct,
pages = {273–284},
numpages = {12},
keywords = {cross-language information retrieval, document alignments, multilingual information retrieval, corpus-based approaches}
}

@article{10.1023/A:1026520926673,
author = {Mittendorf, Elke and Mateev, Bojidar and Sch\"{a}uble, Peter},
title = {Using the Co-Occurrence of Words for Retrieval Weighting},
year = {2000},
issue_date = {October 2000},
publisher = {Kluwer Academic Publishers},
address = {USA},
volume = {3},
number = {3},
issn = {1386-4564},
url = {https://doi.org/10.1023/A:1026520926673},
doi = {10.1023/A:1026520926673},
abstract = {We have applied the well-known Robertson-Sparck Jones weighting to sets of indexing features that are different from word-based features. Our features describe the co-occurrences of words in a window range of predefined size. The experiments have been designed to analyse the value of features that are beyond word-based features but all used retrieval methods can be motivated strictly in the probabilistic framework. Among the several implications of our experiments for weighted retrieval is the surprising result that features that describe the co-occurrences of words in sentence-size or paragraph-size windows are significantly better descriptors than purely word-based indexing features.},
journal = {Inf. Retr.},
month = oct,
pages = {243–251},
numpages = {9},
keywords = {word concurrences, term phase weighting, retrieval routing, probabilistic term weighting}
}

@article{10.1023/A:1026516825764,
author = {Wechsler, Martin and Sch\"{a}uble, Peter},
title = {The Probability Ranking Principle Revisited},
year = {2000},
issue_date = {October 2000},
publisher = {Kluwer Academic Publishers},
address = {USA},
volume = {3},
number = {3},
issn = {1386-4564},
url = {https://doi.org/10.1023/A:1026516825764},
doi = {10.1023/A:1026516825764},
abstract = {A theoretic framework for multimedia information retrieval is introduced which guarantees optimal retrieval effectiveness. In particular, a Ranking Principle for Distributed Multimedia-Documents (RPDM) is described together with an algorithm that satisfies this principle. Finally, the RPDM is shown to be a generalization of the Probability Ranking principle (PRP) which guarantees optimal retrieval effectiveness in the case of text document retrieval. The PRP justifies theoretically the relevance ranking adopted by modern search engines. In contrast to the classical PRP, the new RPDM takes into account transmission and inspection time, and most importantly, aspectual recall rather than simple recall.},
journal = {Inf. Retr.},
month = oct,
pages = {217–227},
numpages = {11},
keywords = {relevance ranking, multimedia information retrieval, optimal search performance, maximum retrieval effectiveness, probability ranking principle}
}

@article{10.1023/A:1026512724855,
author = {Wechsler, Martin and Munteanu, Eugen and Sch\"{a}uble, Peter},
title = {New Approaches to Spoken Document Retrieval},
year = {2000},
issue_date = {October 2000},
publisher = {Kluwer Academic Publishers},
address = {USA},
volume = {3},
number = {3},
issn = {1386-4564},
url = {https://doi.org/10.1023/A:1026512724855},
doi = {10.1023/A:1026512724855},
abstract = {This paper presents four novel techniques for open-vocabulary spoken document retrieval: a method to detect slots that possibly contain a query feature; a method to estimate occurrence probabilities; a technique that we call collection-wide probability re-estimation and a weighting scheme which takes advantage of the fact that long query features are detected more reliably. These four techniques have been evaluated using the TREC-6 spoken document retrieval test collection to determine the improvements in retrieval effectiveness with respect to a baseline retrieval method. Results show that the retrieval effectiveness can be improved considerably despite the large number of speech recognition errors.},
journal = {Inf. Retr.},
month = oct,
pages = {173–188},
numpages = {16},
keywords = {spoken document retrieval, speech recognition, retrieval effectiveness}
}

@article{10.1023/A:1009953814988,
author = {McCallum, Andrew Kachites and Nigam, Kamal and Rennie, Jason and Seymore, Kristie},
title = {Automating the Construction of Internet Portals with Machine Learning},
year = {2000},
issue_date = {July 2000},
publisher = {Kluwer Academic Publishers},
address = {USA},
volume = {3},
number = {2},
issn = {1386-4564},
url = {https://doi.org/10.1023/A:1009953814988},
doi = {10.1023/A:1009953814988},
abstract = {Domain-specific internet portals are growing in popularity because they gather content from the Web and organize it for easy access, retrieval and search. For example, www.campsearch.com allows complex queries by age, location, cost and specialty over summer camps. This functionality is not possible with general, Web-wide search engines. Unfortunately these portals are difficult and time-consuming to maintain. This paper advocates the use of machine learning techniques to greatly automate the creation and maintenance of domain-specific Internet portals. We describe new research in reinforcement learning, information extraction and text classification that enables efficient spidering, the identification of informative text segments, and the population of topic hierarchies. Using these techniques, we have built a demonstration system: a portal for computer science research papers. It already contains over 50,000 papers and is publicly available at www.cora.justresearch.com. These techniques are widely applicable to portal creation in other domains.},
journal = {Inf. Retr.},
month = jul,
pages = {127–163},
numpages = {37},
keywords = {text classification, expectation-maximization, unlabeled data, hidden Markov models, information extraction, reinforcement learning, naive Bayes, spidering, crawling}
}

@article{10.1023/A:1009942513170,
author = {Wermter, Stefan},
title = {Neural Network Agents for Learning Semantic Text Classification},
year = {2000},
issue_date = {July 2000},
publisher = {Kluwer Academic Publishers},
address = {USA},
volume = {3},
number = {2},
issn = {1386-4564},
url = {https://doi.org/10.1023/A:1009942513170},
doi = {10.1023/A:1009942513170},
abstract = {The research project AgNeT develops Agents for Neural Text routing in the internet. Unrestricted potentially faulty text messages arrive at a certain delivery point (e.g. email address or world wide web address). These text messages are scanned and then distributed to one of several expert agents according to a certain task criterium. Possible specific scenarios within this framework include the learning of the routing of publication titles or news titles. In this paper we describe extensive experiments for semantic text routing based on classified library titles and newswire titles. This task is challenging since incoming messages may contain constructions which have not been anticipated. Therefore, the contributions of this research are in learning and generalizing neural architectures for the robust interpretation of potentially noisy unrestricted messages. Neural networks were developed and examined for this topic since they support robustness and learning in noisy unrestricted real-world texts. We describe and compare different sets of experiments. The first set of experiments tests a recurrent neural network for the task of library title classification. Then we describe a larger more difficult newswire classification task from information retrieval. The comparison of the examined models demonstrates that techniques from information retrieval integrated into recurrent plausibility networks performed well even under noise and for different corpora.},
journal = {Inf. Retr.},
month = jul,
pages = {87–103},
numpages = {17},
keywords = {news agent, text classification, neural networks, machine learning, recurrent plausibility network}
}

@article{10.1023/A:1009901830009,
author = {Moghrabi, I. A. R. and Makholian, R. A.},
title = {A New Approach to Clustering Records in Information Retrieval Systems},
year = {2000},
issue_date = {July 2000},
publisher = {Kluwer Academic Publishers},
address = {USA},
volume = {3},
number = {2},
issn = {1386-4564},
url = {https://doi.org/10.1023/A:1009901830009},
doi = {10.1023/A:1009901830009},
abstract = {This work introduces a new approach to record clustering where a hybrid algorithm is presented to cluster records based upon threshold values and the query patterns made to a particular database. The Hamming Distance of a file is used as a measure of space density. The objective of the algorithm is to minimize the Hamming Distance of the file while attaching significance to the most frequent queries being asked. Simulation experiments conducted proved that a great reduction in response time is yielded after the restructuring of a file. We study the space density properties of a file and how it affects retrieval time before and after clustering, as a means of predicting file performance and making appropriate choices of parameters. Criteria, such as, block size, threshold value, percentage of records satisfying a given set of queries, etc., which affect clustering and response time are also studied.},
journal = {Inf. Retr.},
month = jul,
pages = {105–126},
numpages = {22},
keywords = {information retrieval, database systems, statistical analysis, clustering}
}

@article{10.1023/A:1013002601898,
author = {Moffat, Alistair and Stuiver, Lang},
title = {Binary Interpolative Coding for Effective Index Compression},
year = {2000},
issue_date = {July 2000},
publisher = {Kluwer Academic Publishers},
address = {USA},
volume = {3},
number = {1},
issn = {1386-4564},
url = {https://doi.org/10.1023/A:1013002601898},
doi = {10.1023/A:1013002601898},
abstract = {Information retrieval systems contain large volumes of text, and currently have typical sizes into the gigabyte range. Inverted indexes are one important method for providing search facilities into these collections, but unless compressed require a great deal of space. In this paper we introduce a new method for compressing inverted indexes that yields excellent compression, fast decoding, and exploits clustering—the tendency for words to appear relatively frequently in some parts of the collection and infrequently in others. We also describe two other quite separate applications for the same compression method: representing the MTF list positions generated by the Burrows-Wheeler Block Sorting transformation; and transmitting the codebook for semi-static block-based minimum-redundancy coding.},
journal = {Inf. Retr.},
month = jul,
pages = {25–47},
numpages = {23},
keywords = {document database, context-based model, index compression}
}

@article{10.1023/A:1009973300990,
author = {Zobel, Justin},
title = {Guest Introduction},
year = {2000},
issue_date = {July 2000},
publisher = {Kluwer Academic Publishers},
address = {USA},
volume = {3},
number = {1},
issn = {1386-4564},
url = {https://doi.org/10.1023/A:1009973300990},
doi = {10.1023/A:1009973300990},
journal = {Inf. Retr.},
month = jul,
pages = {5–6},
numpages = {2}
}

@article{10.1023/A:1009934302807,
author = {Navarro, Gonzalo and De Moura, Edleno Silva and Neubert, Marden and Ziviani, Nivio and Baeza-Yates, Ricardo},
title = {Adding Compression to Block Addressing Inverted Indexes},
year = {2000},
issue_date = {July 2000},
publisher = {Kluwer Academic Publishers},
address = {USA},
volume = {3},
number = {1},
issn = {1386-4564},
url = {https://doi.org/10.1023/A:1009934302807},
doi = {10.1023/A:1009934302807},
abstract = {Inverted index compression, block addressing and sequential search on compressed text are three techniques that have been separately developed for efficient, low-overhead text retrieval. Modern text compression techniques can reduce the text to less than 30% of its size and allow searching it directly and faster than the uncompressed text. Inverted index compression obtains significant reduction of its original size at the same processing speed. Block addressing makes the inverted lists point to text blocks instead of exact positions and pay the reduction in space with some sequential text scanning.In this work we combine the three ideas in a single scheme. We present a compressed inverted file that indexes compressed text and uses block addressing. We consider different techniques to compress the index and study their performance with respect to the block size. We compare the index against three separate techniques for varying block sizes, showing that our index is superior to each isolated approach. For instance, with just 4% of extra space overhead the index has to scan less than 12% of the text for exact searches and about 20% allowing one error in the matches.},
journal = {Inf. Retr.},
month = jul,
pages = {49–77},
numpages = {29},
keywords = {text compression, block addressing, text databases, inverted files}
}

@article{10.1023/A:1009910017828,
author = {Klein, Shmuel T.},
title = {Skeleton Trees for the Efficient Decoding of Huffman Encoded Texts},
year = {2000},
issue_date = {July 2000},
publisher = {Kluwer Academic Publishers},
address = {USA},
volume = {3},
number = {1},
issn = {1386-4564},
url = {https://doi.org/10.1023/A:1009910017828},
doi = {10.1023/A:1009910017828},
abstract = {A new data structure is investigated, which allows fast decoding of texts encoded by canonical Huffman codes. The storage requirements are much lower than for conventional Huffman trees, O(log ^2 n) for trees of depth O(log n), and decoding is faster, because a part of the bit-comparisons necessary for the decoding may be saved. Empirical results on large real-life distributions show a reduction of up to 50% and more in the number of bit operations. The basic idea is then generalized, yielding further savings.},
journal = {Inf. Retr.},
month = jul,
pages = {7–23},
numpages = {17},
keywords = {skeleton trees, canonical Huffman codes, decoding, Huffman trees}
}

@article{10.1023/A:1009976227802,
author = {Turney, Peter D.},
title = {Learning Algorithms for Keyphrase Extraction},
year = {2000},
issue_date = {May 2000},
publisher = {Kluwer Academic Publishers},
address = {USA},
volume = {2},
number = {4},
issn = {1386-4564},
url = {https://doi.org/10.1023/A:1009976227802},
doi = {10.1023/A:1009976227802},
abstract = {Many academic journals ask their authors to provide a list of about five to fifteen keywords, to appear on the first page of each article. Since these key words are often phrases of two or more words, we prefer to call them keyphrases. There is a wide variety of tasks for which keyphrases are useful, as we discuss in this paper. We approach the problem of automatically extracting keyphrases from text as a supervised learning task. We treat a document as a set of phrases, which the learning algorithm must learn to classify as positive or negative examples of keyphrases. Our first set of experiments applies the C4.5 decision tree induction algorithm to this learning task. We evaluate the performance of nine different configurations of C4.5. The second set of experiments applies the GenEx algorithm to the task. We developed the GenEx algorithm specifically for automatically extracting keyphrases from text. The experimental results support the claim that a custom-designed algorithm (GenEx), incorporating specialized procedural domain knowledge, can generate better keyphrases than a general-purpose algorithm (C4.5). Subjective human evaluation of the keyphrases generated by GenEx suggests that about 80% of the keyphrases are acceptable to human readers. This level of performance should be satisfactory for a wide variety of applications.},
journal = {Inf. Retr.},
month = may,
pages = {303–336},
numpages = {34},
keywords = {indexing, keywords, summarization, keyphrase extraction, machine learning}
}

@article{10.1023/A:1009932512781,
author = {Jones, Gareth and Sakai, Tetsuya and Kajiura, Masahiro and Sumita, Kazuo},
title = {Incremental Relevance Feedback in Japanese Text Retrieval},
year = {2000},
issue_date = {May 2000},
publisher = {Kluwer Academic Publishers},
address = {USA},
volume = {2},
number = {4},
issn = {1386-4564},
url = {https://doi.org/10.1023/A:1009932512781},
doi = {10.1023/A:1009932512781},
abstract = {The application of relevance feedback techniques has been shown to improve retrieval performance for a number of information retrieval tasks. This paper explores incremental relevance feedback for ad hoc Japanese text retrieval; examining, separately and in combination, the utility of term reweighting and query expansion using a probabilistic retrieval model. Retrieval performance is evaluated in terms of standard precision-recall measures, and also using “number-to-view” graphs. Experimental results, on the standard BMIR-J2 Japanese language retrieval collection, show that both term reweighting and query expansion improve retrieval performance. This is reflected in improvements in both precision and recall, but also a reduction in the average number of documents which must be viewed to find a selected number of relevant items. In particular, using a simple simulation of user searching, incremental application of relevance information is shown to lead to progressively improved retrieval performance and an overall reduction in the number of documents that a user must view to find relevant ones.},
journal = {Inf. Retr.},
month = may,
pages = {361–384},
numpages = {24},
keywords = {probabilistic retrieval, incremental relevance feedback, query expansion, term reweighting, number-to-view graphs, Japanese text}
}

@article{10.1023/A:1009928328710,
author = {Wondergem, B. C. M. and van Bommel, P. and van der Weide, Th. P.},
title = {Matching Index Expressions for Information Retrieval},
year = {2000},
issue_date = {May 2000},
publisher = {Kluwer Academic Publishers},
address = {USA},
volume = {2},
number = {4},
issn = {1386-4564},
url = {https://doi.org/10.1023/A:1009928328710},
doi = {10.1023/A:1009928328710},
abstract = {The INN system is a dynamic hypertext tool for searching and exploring the WWW. It uses a dynamically built ancillary layer to support easy interaction. This layer features the subexpressions of index expressions that are extracted from rendered documents. Currently, the INN system uses keyword based matching. The effectiveness of the INN system may be increased by using matching functions for index expressions. In the design of such functions, several constraints stemming from the INN must be taken into account. Important constraints are a limited response time and storage space, a focus on discriminating (different notions of) subexpressions for index expressions, and domain independency. With these contextual constraints in mind, several matching functions are designed and both theoretically and practically evaluated.},
journal = {Inf. Retr.},
month = may,
pages = {337–360},
numpages = {24},
keywords = {information retrieval, index expressions, similarity, matching}
}

@article{10.1023/A:1009915010963,
author = {Hughey, M. K. and Berry, M. W.},
title = {Improved Query Matching Using Kd-Trees: A Latent Semantic Indexing Enhancement},
year = {2000},
issue_date = {May 2000},
publisher = {Kluwer Academic Publishers},
address = {USA},
volume = {2},
number = {4},
issn = {1386-4564},
url = {https://doi.org/10.1023/A:1009915010963},
doi = {10.1023/A:1009915010963},
abstract = {Efficient information searching and retrieval methods are needed to navigate the ever increasing volumes of digital information. Traditional lexical information retrieval methods can be inefficient and often return inaccurate results. To overcome problems such as polysemy and synonymy, concept-based retrieval methods have been developed. One such method is Latent Semantic Indexing (LSI), a vector-space model, which uses the singular value decomposition (SVD) of a term-by-document matrix to represent terms and documents in k-dimensional space. As with other vector-space models, LSI is an attempt to exploit the underlying semantic structure of word usage in documents. During the query matching phase of LSI, a user's query is first projected into the term-document space, and then compared to all terms and documents represented in the vector space. Using some similarity measure, the nearest (most relevant) terms and documents are identified and returned to the user. The current LSI query matching method requires that the similarity measure be computed between the query and every term and document in the vector space. In this paper, the kd-tree searching algorithm is used within a recent LSI implementation to reduce the time and computational complexity of query matching. The kd-tree data structure stores the term and document vectors in such a way that only those terms and documents that are most likely to qualify as nearest neighbors to the query will be examined and retrieved.},
journal = {Inf. Retr.},
month = may,
pages = {287–302},
numpages = {16},
keywords = {latent semantic indexing, kd-trees, query matching, vector-space model}
}

@article{10.1023/A:1009962928226,
author = {Srihari, Rohini K. and Zhang, Zhongfei and Rao, Aibing},
title = {Intelligent Indexing and Semantic Retrieval of Multimodal Documents},
year = {2000},
issue_date = {May 2000},
publisher = {Kluwer Academic Publishers},
address = {USA},
volume = {2},
number = {2–3},
issn = {1386-4564},
url = {https://doi.org/10.1023/A:1009962928226},
doi = {10.1023/A:1009962928226},
abstract = {Finding useful information from large multimodal document collections such as the WWW without encountering numerous false positives poses a challenge to multimedia information retrieval systems (MMIR). This research addresses the problem of finding pictures. The fact that images do not appear in isolation, but rather with accompanying, collateral text is exploited. Taken independently, existing techniques for picture retrieval using (i) text-based and (ii) image-based methods have several limitations. This research presents a general model for multimodal information retrieval that addresses the following issues: (i) users' information need, (ii) expressing information need through composite, multimodal queries, and (iii) determining the most appropriate weighted combination of indexing techniques in order to best satisfy information need. A machine learning approach is proposed for the latter. The focus is on improving precision and recall in a MMIR system by optimally combining text and image similarity. Experiments are presented which demonstrate the utility of individual indexing systems in improving overall average precision.},
journal = {Inf. Retr.},
month = may,
pages = {245–275},
numpages = {31},
keywords = {text indexing, image indexing, content-based retrieval, multimodal query processing, multimedia information retrieval}
}

@article{10.1023/A:1009958827317,
author = {Williams, William J. and Zalubas, Eugene J. and Hero, Alfred O.},
title = {Word Spotting in Bitmapped Fax Documents},
year = {2000},
issue_date = {May 2000},
publisher = {Kluwer Academic Publishers},
address = {USA},
volume = {2},
number = {2–3},
issn = {1386-4564},
url = {https://doi.org/10.1023/A:1009958827317},
doi = {10.1023/A:1009958827317},
abstract = {Images and signals may be represented by forms invariant to time shifts, spatial shifts, frequency shifts, and scale changes. Advances in time-frequency analysis and scale transform techniques have made this possible. However, factors such as noise contamination and “style” differences complicate this. An example is found in text, where letters and words may vary in size and position. Examples of complicating variations include the font used, corruption during facsimile (fax) transmission, and printer characteristics. The solution advanced in this paper is to cast the desired invariants into separate subspaces for each extraneous factor or group of factors. The first goal is to have minimal overlap between these subspaces and the second goal is to be able to identify each subspace accurately. Concepts borrowed from high-resolution spectral analysis, but adapted uniquely to this problem have been found to be useful in this context. Once the pertinent subspace is identified, the recognition of a particular invariant form within this subspace is relatively simple using well-known singular value decomposition (SVD) techniques. The basic elements of the approach can be applied to a variety of pattern recognition problems. The specific application covered in this paper is word spotting in bitmapped fax documents.},
journal = {Inf. Retr.},
month = may,
pages = {207–226},
numpages = {20},
keywords = {scale, invariant, word spotting, position, facsimile}
}

@article{10.1023/A:1009958408662,
author = {Baird, Henry S. and Chen, Francine R.},
title = {Introduction},
year = {2000},
issue_date = {May 2000},
publisher = {Kluwer Academic Publishers},
address = {USA},
volume = {2},
number = {2–3},
issn = {1386-4564},
url = {https://doi.org/10.1023/A:1009958408662},
doi = {10.1023/A:1009958408662},
journal = {Inf. Retr.},
month = may,
pages = {139–140},
numpages = {2}
}

@article{10.1023/A:1009954710479,
author = {Lopresti, Daniel and Zhou, Jiangying},
title = {Locating and Recognizing Text in WWW Images},
year = {2000},
issue_date = {May 2000},
publisher = {Kluwer Academic Publishers},
address = {USA},
volume = {2},
number = {2–3},
issn = {1386-4564},
url = {https://doi.org/10.1023/A:1009954710479},
doi = {10.1023/A:1009954710479},
abstract = {The explosive growth of the World Wide Web has resulted in a distributed database consisting of hundreds of millions of documents. While existing search engines index a page based on the text that is readily extracted from its HTML encoding, an increasing amount of the information on the Web is embedded in images. This situation presents a new and exciting challenge for the fields of document analysis and information retrieval, as WWW image text is typically rendered in color and at very low spatial resolutions. In this paper, we survey the results of several years of our work in the area. For the problem of locating text in Web images, we describe a procedure based on clustering in color space followed by a connected-components analysis that seems promising. For character recognition, we discuss techniques using polynomial surface fitting and “fuzzy” n-tuple classifiers. Also presented are the results of several experiments that demonstrate where our methods perform well and where more work needs to be done. We conclude with a discussion of topics for further research.},
journal = {Inf. Retr.},
month = may,
pages = {177–206},
numpages = {30},
keywords = {document analysis, WWW image text, optical character recognition, information retrieval}
}

@article{10.1023/A:1009950525500,
author = {Mitra, M. and Chaudhuri, B. B.},
title = {Information Retrieval from Documents: A Survey},
year = {2000},
issue_date = {May 2000},
publisher = {Kluwer Academic Publishers},
address = {USA},
volume = {2},
number = {2–3},
issn = {1386-4564},
url = {https://doi.org/10.1023/A:1009950525500},
doi = {10.1023/A:1009950525500},
abstract = {Given the phenomenal growth in the variety and quantity of data available to users through electronic media, there is a great demand for efficient and effective ways to organize and search through all this information. Besides speech, our principal means of communication is through visual media, and in particular, through documents. In this paper, we provide an update on Doermann's comprehensive survey (1998) of research results in the broad area of document-based information retrieval. The scope of this survey is also somewhat broader, and there is a greater emphasis on relating document image analysis methods to conventional IR methods.Documents are available in a wide variety of formats. Technical papers are often available as ASCII files of clean, correct, text. Other documents may only be available as hardcopies. These documents have to be scanned and stored as images so that they may be processed by a computer. The textual content of these documents may also be extracted and recognized using OCR methods. Our survey covers the broad spectrum of methods that are required to handle different formats like text and images. The core of the paper focuses on methods that manipulate document images directly, and perform various information processing tasks such as retrieval, categorization, and summarization, without attempting to completely recognize the textual content of the document. We start, however, with a brief overview of traditional IR techniques that operate on clean text. We also discuss research dealing with text that is generated by running OCR on document images. Finally, we also briefly touch on the related problem of content-based image retrieval.},
journal = {Inf. Retr.},
month = may,
pages = {141–163},
numpages = {23},
keywords = {document image analysis, image retrieval, optical character recognition, text retrieval}
}

@article{10.1023/A:1009910911387,
author = {Hu, Jianying and Kashi, Ramanujan and Wilfong, Gordon},
title = {Comparison and Classification of Documents Based on Layout Similarity},
year = {2000},
issue_date = {May 2000},
publisher = {Kluwer Academic Publishers},
address = {USA},
volume = {2},
number = {2–3},
issn = {1386-4564},
url = {https://doi.org/10.1023/A:1009910911387},
doi = {10.1023/A:1009910911387},
abstract = {This paper describes features and methods for document image comparison and classification at the spatial layout level. The methods are useful for visual similarity based document retrieval as well as fast algorithms for initial document type classification without OCR. A novel feature set called interval encoding is introduced to capture elements of spatial layout. This feature set encodes region layout information in fixed-length vectors by capturing structural characteristics of the image. These fixed-length vectors are then compared to each other through a Manhattan distance computation for fast page layout comparison. The paper describes experiments and results to rank-order a set of document pages in terms of their layout similarity to a test document. We also demonstrate the usefulness of the features derived from interval coding in a hidden Markov model based page layout classification system that is trainable and extendible. The methods described in the paper can be used in various document retrieval tasks including visual similarity based retrieval, categorization and information extraction.},
journal = {Inf. Retr.},
month = may,
pages = {227–243},
numpages = {17},
keywords = {edit distance, Manhattan distance, document retrieval, hidden Markov models, dynamic warping, clustering, document classification}
}

@article{10.1023/A:1009902609570,
author = {Kantor, Paul B. and Voorhees, Ellen M.},
title = {The TREC-5 Confusion Track: Comparing Retrieval Methods for Scanned Text},
year = {2000},
issue_date = {May 2000},
publisher = {Kluwer Academic Publishers},
address = {USA},
volume = {2},
number = {2–3},
issn = {1386-4564},
url = {https://doi.org/10.1023/A:1009902609570},
doi = {10.1023/A:1009902609570},
abstract = {A known-item search is a particular information retrieval task in which the system is asked to find a single target document in a large document set. The TREC-5 confusion track used a set of 49 known-item tasks to study the impact of data corruption on retrieval system performance. Two corrupted versions of a 55,600 document corpus whose true content was known were created by applying OCR techniques to page images. The first version of the corpus used the page images as scanned, resulting in an estimated character error rate of approximately 5%. The second version used page images that had been down-sampled, resulting in an estimated character error rate of approximately 20%. The true text and each of the corrupted versions were then searched using the same set of 49 questions. In general, retrieval methods that attempted a probabilistic reconstruction of the original clean text fared better than methods that simply accepted corrupted versions of the query text.},
journal = {Inf. Retr.},
month = may,
pages = {165–176},
numpages = {12},
keywords = {optical character recognition (OCR), text retrieval, evaluation, TREC}
}

@article{10.1023/A:1009998002873,
author = {Ruthven, Ian},
title = {Incorporating Aspects of Information Use into Relevance Feedback},
year = {2000},
issue_date = {February 2000},
publisher = {Kluwer Academic Publishers},
address = {USA},
volume = {2},
number = {1},
issn = {1386-4564},
url = {https://doi.org/10.1023/A:1009998002873},
doi = {10.1023/A:1009998002873},
abstract = {In this paper we look at some of the problems in interacting with
best-match retrieval systems. In particular, we examine the areas of
interaction, some investigations of the complexity and breadth of
interaction and attempts to categorise user's information seeking
behaviour. We suggest that one of the difficulties of traditional IR
systems in supporting information seeking is the way the information
content of documents is represented. We discuss an alternative
representation, based on how information is used within
documents.},
journal = {Inf. Retr.},
month = feb,
pages = {83–88},
numpages = {6},
keywords = {relevance feedback, information seeking, interaction}
}

@article{10.1023/A:1009989801965,
author = {Adriani, Mirna},
title = {Using Statistical Term Similarity for Sense Disambiguationin Cross-Language Information Retrieval},
year = {2000},
issue_date = {February 2000},
publisher = {Kluwer Academic Publishers},
address = {USA},
volume = {2},
number = {1},
issn = {1386-4564},
url = {https://doi.org/10.1023/A:1009989801965},
doi = {10.1023/A:1009989801965},
abstract = {With the increasing availability of machine-readable
bilingual dictionaries, dictionary-based automatic query translation
has become a viable approach to Cross-Language Information Retrieval
(CLIR). In this approach, resolving term ambiguity is a crucial step.
We propose a sense disambiguation technique based on a
term-similarity measure for selecting the right translation sense of
a query term. In addition, we apply a query expansion technique which
is also based on the term similarity measure to improve the
effectiveness of the translation queries. The results of our
Indonesian to English and English to Indonesian CLIR experiments
demonstrate the effectiveness of the sense disambiguation technique.
As for the query expansion technique, it is shown to be effective as
long as the term ambiguity in the queries has been resolved. In the
effort to solve the term ambiguity problem, we discovered that
differences in the pattern of word-formation between the two
languages render query translations from one language to the other
difficult.},
journal = {Inf. Retr.},
month = feb,
pages = {71–82},
numpages = {12},
keywords = {term disambiguation, cross-language information retrieval}
}

@article{10.1023/A:1009973415168,
author = {Crestani, Fabio},
title = {Exploiting the Similarity of Non-Matching Terms at RetrievalTime},
year = {2000},
issue_date = {February 2000},
publisher = {Kluwer Academic Publishers},
address = {USA},
volume = {2},
number = {1},
issn = {1386-4564},
url = {https://doi.org/10.1023/A:1009973415168},
doi = {10.1023/A:1009973415168},
abstract = {In classic Information Retrieval systems a relevant
document will not be retrieved in response to a query if the document
and query representations do not share at least one term. This
problem, known as “term mismatch”, has been recognised for a long
time by the Information Retrieval community and a number of possible
solutions have been proposed. Here I present a preliminary
investigation into a new class of retrieval models that attempt to
solve the term mismatch problem by exploiting complete or partial
knowledge of term similarity in the term space. The use of term
similarity enables to enhance classic retrieval models by taking into
account non-matching terms. The theoretical advantages and drawbacks
of these models are presented and compared with other models tackling
the same problem. A preliminary experimental investigation into the
performance gain achieved by exploiting term similarity with the
proposed models is presented and discussed.},
journal = {Inf. Retr.},
month = feb,
pages = {27–47},
numpages = {21},
keywords = {term similarity, information retrieval, retrieval model, term mismatch problem}
}

@article{10.1023/A:1009969229281,
author = {van Rijsbergen, C. J.},
title = {Another Look at the Logical Uncertainty Principle},
year = {2000},
issue_date = {February 2000},
publisher = {Kluwer Academic Publishers},
address = {USA},
volume = {2},
number = {1},
issn = {1386-4564},
url = {https://doi.org/10.1023/A:1009969229281},
doi = {10.1023/A:1009969229281},
abstract = {The Logical Uncertainty Principle is
re-examined from the point of classical logic. Two interpretations
are given, an objective one in terms of an axiomatic theory of information,
and a subjective one based on Ramsey‘s theory of probability.},
journal = {Inf. Retr.},
month = feb,
pages = {17–26},
numpages = {10},
keywords = {information theory, information retrieval model, Ramsey test, uncertainty, Bayesian inference}
}

@article{10.1023/A:1009933700147,
author = {Sanderson, Mark},
title = {Retrieving with Good Sense},
year = {2000},
issue_date = {February 2000},
publisher = {Kluwer Academic Publishers},
address = {USA},
volume = {2},
number = {1},
issn = {1386-4564},
url = {https://doi.org/10.1023/A:1009933700147},
doi = {10.1023/A:1009933700147},
abstract = {Although always present in text, word sense
ambiguity only recently became regarded as a
problem to information retrieval which was
potentially solvable. The growth of interest in
word senses resulted from new directions taken in
disambiguation research. This paper first outlines
this research and surveys the resulting efforts in
information retrieval. Although the majority of
attempts to improve retrieval effectiveness were
unsuccessful, much was learnt from the research.
Most notably a notion of under what circumstance
disambiguation may prove of use to retrieval.},
journal = {Inf. Retr.},
month = feb,
pages = {49–69},
numpages = {21},
keywords = {information retrieval, word sense ambiguity, word sense disambiguation, stemming}
}

@article{10.1023/A:1009906420620,
author = {Reid, Jane},
title = {A Task-Oriented Non-Interactive Evaluation Methodologyfor Information Retrieval Systems},
year = {2000},
issue_date = {February 2000},
publisher = {Kluwer Academic Publishers},
address = {USA},
volume = {2},
number = {1},
issn = {1386-4564},
url = {https://doi.org/10.1023/A:1009906420620},
doi = {10.1023/A:1009906420620},
abstract = {Past research has identified many different types of
relevance in information retrieval (IR). So far, however, most
evaluation of IR systems has been through batch experiments conducted
with test collections containing only expert, topical relevance
judgements. Recently, there has been some movement away from this
traditional approach towards interactive, more user-centred methods
of evaluation. However, these are expensive for evaluators in terms
both of time and of resources. This paper describes a new evaluation
methodology, using a task-oriented test collection, which combines
the advantages of traditional non-interactive testing with a more
user-centred emphasis. The main features of a task-oriented test
collection are the adoption of the task, rather than the query, as
the primary unit of evaluation and the naturalistic character of the
relevance judgements.},
journal = {Inf. Retr.},
month = feb,
pages = {115–129},
numpages = {15},
keywords = {user-centred evaluation, test collection, nature of relevance, task framework}
}

@article{10.1023/A:1009905228372,
author = {Dunlop, Mark and Lalmas, Mounia},
title = {Introduction},
year = {2000},
issue_date = {February 2000},
publisher = {Kluwer Academic Publishers},
address = {USA},
volume = {2},
number = {1},
issn = {1386-4564},
url = {https://doi.org/10.1023/A:1009905228372},
doi = {10.1023/A:1009905228372},
journal = {Inf. Retr.},
month = feb,
pages = {9–15},
numpages = {7}
}

@article{10.1023/A:1009902203782,
author = {Campbell, Iain},
title = {Interactive Evaluation of the Ostensive ModelUsing a New Test Collection of Images with Multiple Relevance Assessments},
year = {2000},
issue_date = {February 2000},
publisher = {Kluwer Academic Publishers},
address = {USA},
volume = {2},
number = {1},
issn = {1386-4564},
url = {https://doi.org/10.1023/A:1009902203782},
doi = {10.1023/A:1009902203782},
abstract = {The Ostensive Model proposes a manner of structuring
the uncertainty associated with individual relevance
judgements as sources of evidence in relevance feedback. It
proposes temporal profiles of uncertainty, motivating the application
of a particular class of discount function with respect to the age of
the evidence. This paper presents an initial evaluation of the relative
effectiveness of different uncertainty discount functions.A novel direct manipulation interface to a multimedia
retrieval system embodying the Ostensive Model is outlined briefly. The
paper describes the construction and
characteristics of a new image test collection utilising multiple
binary relevance assessments. The use of such multiple assessments
and multiple interpretations of them are discussed. The evaluation
environment is detailed in terms of the
interface, test collection, and tasks set to users. Multiple
interpretations of the results, and the statistical significance
of comparisons are presented.The results obtained in the evaluation are consistent
with the proposals of the
Ostensive Model—reinforcing a particular evidence profile. The
results give clear pointers to further, more specific, evaluations.},
journal = {Inf. Retr.},
month = feb,
pages = {89–114},
numpages = {26},
keywords = {fisheye, evidence discounting, relevance feedback, Ostensive Model, image test collection, browsing, interactive evaluation, ostension}
}

@article{10.1023/A:1009995816485,
author = {Markkula, Marjo and Sormunen, Eero},
title = {End-User Searching Challenges Indexing Practices Inthe Digital Newspaper Photo Archive},
year = {2000},
issue_date = {January 2000},
publisher = {Kluwer Academic Publishers},
address = {USA},
volume = {1},
number = {4},
issn = {1386-4564},
url = {https://doi.org/10.1023/A:1009995816485},
doi = {10.1023/A:1009995816485},
abstract = {Previous research in conceptual indexing methods of images
has furnished us with refined
theoretical frameworks characterising various aspects of
images that could and should be indexed
using textual descriptors. The development of digital image
processing technologies has bred a
brigade of content-based indexing and retrieval methods
available for applications. What the users
need and in what kinds of environments different indexing and
retrieval methods are relevant, has
remained an area of less intensive research work.This article presents the results of a field study
concentrating on journalists as users of a
digital newspaper photo archive. The expressed photo needs,
applied selection criteria and
observed searching behaviours in journalists' daily work were
contrasted with the indexing
practices applied by the archivists. The results showed that
the journalists achieved satisfactory
results when trivial query terms were available, e.g. when
photos of named persons were needed.
Browsing was the main searching strategy applied by the
journalists, but the system did not
support browsing well. The access problems faced by the users
in particular photo needs are discussed in detail. The paper
concludes by discussing the potential approaches in developing both
the concept-based and content-based indexing methods as well
as the user interfaces in photo retrieval systems.},
journal = {Inf. Retr.},
month = jan,
pages = {259–285},
numpages = {27},
keywords = {indexing practices, user needs, newspaper photographs, image retrieval, searching behavior}
}

@article{10.1023/A:1009983401464,
author = {Kek\"{a}l\"{a}Inen, Jaana and J\"{a}rvelin, Kalervo},
title = {The Co-Effects of Query Structure and Expansion on RetrievalPerformance in Probabilistic Text Retrieval},
year = {2000},
issue_date = {January 2000},
publisher = {Kluwer Academic Publishers},
address = {USA},
volume = {1},
number = {4},
issn = {1386-4564},
url = {https://doi.org/10.1023/A:1009983401464},
doi = {10.1023/A:1009983401464},
abstract = {The effects of query structures and query expansion (QE) on retrieval performance were tested with a best match retrieval system (InQuery ^1 ). Query structure means the use of operators to express the relations between search keys. Six different structures were tested, representing strong structures (e.g., queries with facets or concepts identified) and weak structures (no concepts identified, a query is ‘a bag of search keys'). QE was based on concepts, which were first selected from a searching thesaurus, and then expanded by semantic relationships given in the thesaurus. The expansion levels were (a) no expansion, (b) a synonym expansion, (c) a narrower concept expansion, (d) an associative concept expansion, and (e) a cumulative expansion of all other expansions. With weak structures and Boolean structured queries, QE was not very effective. The best performance was achieved with a combination of a facet structure, where search keys within a facet were treated as instances of one search key (the SYN operator), and the largest expansion.},
journal = {Inf. Retr.},
month = jan,
pages = {329–344},
numpages = {16},
keywords = {query structures, text retrieval, query expansion, concept-based query formulation, semantic conceptual relationships}
}

@article{10.1023/A:1009979200555,
author = {Frost, C. Olivia and Taylor, Bradley and Noakes, Anna and Markel, Stephen and Torres, Deborah and Drabenstott, Karen M.},
title = {Browse and Search Patterns in a Digital Image Database},
year = {2000},
issue_date = {January 2000},
publisher = {Kluwer Academic Publishers},
address = {USA},
volume = {1},
number = {4},
issn = {1386-4564},
url = {https://doi.org/10.1023/A:1009979200555},
doi = {10.1023/A:1009979200555},
abstract = {A prototype image retrieval system with browse and search
capabilities was developed to investigate patterns of searching a
collection of digital visual images, as well as factors, such as
image size, resolution, and download speed, which affect browsing.
The subject populations were art history specialists and
non-specialists. Through focus group interviews, a controlled test,
post-test interviews and an online survey, data was gathered to
compare preferences and actual patterns of use in browsing and
searching. While specialists preferred direct search to browsing, and
generalists used browsing as their preferred mode, both user groups
found each mode to play a role depending on information need, and
found value in a system combining both browse and direct search.
There were no significant differences in performance among the search
modes of browse, search, and combined browse/search models when the
quasi-controlled study tested the different modes.},
journal = {Inf. Retr.},
month = jan,
pages = {287–313},
numpages = {27},
keywords = {user studies, image retrieval, browsing, digital images}
}

@article{10.1023/A:1009931317394,
author = {Bookstein, A. and Klein, St and Raita, T.},
title = {Simple Bayesian Model for Bitmap Compression},
year = {2000},
issue_date = {January 2000},
publisher = {Kluwer Academic Publishers},
address = {USA},
volume = {1},
number = {4},
issn = {1386-4564},
url = {https://doi.org/10.1023/A:1009931317394},
doi = {10.1023/A:1009931317394},
abstract = {Bitmaps are a useful, but storage voracious, component of many
information retrieval systems. Earlier efforts to compress bitmaps
were based on models of bit generation, particularly Markov models.
While these permitted considerable reduction in storage, the short
memory of Markov models may limit their compression efficiency. In
this paper we accept the state orientation of Markov models, but
introduce a Bayesian approach to assess the state; the analysis is
based on data accumulating in a growing window. The paper describes
the details of the probabilistic assumptions governing the Bayesian
analysis, as well as the protocol for controlling the window that
receives the data. We find slight improvement over the best
performing strictly Markov models.},
journal = {Inf. Retr.},
month = jan,
pages = {315–328},
numpages = {14},
keywords = {concordances, bitmap compression, IR models, Markov modelling}
}

@article{10.1023/A:1009983522080,
author = {Weigend, Andreas S. and Wiener, Erik D. and Pedersen, Jan O.},
title = {Exploiting Hierarchy in Text Categorization},
year = {1999},
issue_date = {October 1999},
publisher = {Kluwer Academic Publishers},
address = {USA},
volume = {1},
number = {3},
issn = {1386-4564},
url = {https://doi.org/10.1023/A:1009983522080},
doi = {10.1023/A:1009983522080},
abstract = {With the recent dramatic increase in electronic access to
documents, text categorization—the task of assigning topics to a
given document—has moved to the center of the information sciences
and knowledge management. This article uses the structure that is
present in the semantic space of topics in order to improve
performance in text categorization: according to their meaning,
topics can be grouped together into “meta-topics”, e.g., gold,
silver, and copper are all metals. The proposed architecture matches
the hierarchical structure of the topic space, as opposed to a flat
model that ignores the structure. It accommodates both single and
multiple topic assignments for each document. Its probabilistic
interpretation allows its predictions to be combined in a principled
way with information from other sources. The first level of the
architecture predicts the probabilities of the meta-topic groups.
This allows the individual models for each topic on the second level
to focus on finer discriminations within the group. Evaluating the
performance of a two-level implementation on the Reuters-22173
testbed of newswire articles shows the most significant improvement
for rare classes.},
journal = {Inf. Retr.},
month = oct,
pages = {193–216},
numpages = {24},
keywords = {machine learning, neural networks, text categorization, text mining, problem decomposition, performance evaluation, information retrieval, hierarchical models, probabilistic models, topic spotting, knowledge management}
}

@article{10.1023/A:1009980820262,
author = {Vogt, Christopher C. and Cottrell, Garrison W.},
title = {Fusion Via a Linear Combination of Scores},
year = {1999},
issue_date = {October 1999},
publisher = {Kluwer Academic Publishers},
address = {USA},
volume = {1},
number = {3},
issn = {1386-4564},
url = {https://doi.org/10.1023/A:1009980820262},
doi = {10.1023/A:1009980820262},
abstract = {We present a thorough analysis of the capabilities of the linear
combination (LC) model for fusion of information retrieval systems.
The LC model combines the results lists of multiple IR systems by
scoring each document using a weighted sum of the scores from each of
the component systems. We first present both empirical and analytical
justification for the hypotheses that such a model should only be used
when the systems involved have high performance, a large overlap of
relevant documents, and a small overlap of nonrelevant documents. The
empirical approach allows us to very accurately predict the
performance of a combined system. We also derive a formula for a
theoretically optimal weighting scheme for combining 2 systems. We
introduce d—the difference between the average score on relevant
documents and the average score on nonrelevant documents—as a
performance measure which not only allows mathematical reasoning about
system performance, but also allows the selection of weights which
generalize well to new documents. We describe a number of experiments
involving large numbers of different IR systems which support these
findings.},
journal = {Inf. Retr.},
month = oct,
pages = {151–173},
numpages = {23},
keywords = {routing, performance evaluation, neural networks, linear combination, fusion}
}

@article{10.1023/A:1009939707058,
author = {Pirkola, Ari and Keskustalo, Heikki and J\"{a}rvelin, Kalervo},
title = {The Effects of Conjunction, Facet Structure, and Dictionary Combinations in Concept-Based Cross-Language Retrieval},
year = {1999},
issue_date = {October 1999},
publisher = {Kluwer Academic Publishers},
address = {USA},
volume = {1},
number = {3},
issn = {1386-4564},
url = {https://doi.org/10.1023/A:1009939707058},
doi = {10.1023/A:1009939707058},
abstract = {The paper studies concept-based cross-language information retrieval
(CLIR). The document collection was a subset of the TREC collection. The test
requests were formed from TREC‘s health related topics. As translation
dictionaries the study used a general dictionary and a domain-specific
(=medical)
dictionary. The effects of translation method, conjunction, and facet order on
the effectiveness of concept-based cross-language queries were studied, and
concept-based structuring of cross-language queries was compared to mechanical
structuring based on the output of dictionaries. The performance of translated
Finnish queries against English documents was compared to the performance of
original English queries against the English documents, and the performance of
different CLIR query types was compared with one another. No major
difference was
found between concept-based and mechanical structuring. The best translation
method was a simultaneous look-up in the medical dictionary and the general
dictionary, in which case cross-language queries performed as well as
the original
English queries. The results showed that especially at high exhaustivity (the
number of mutually restrictive concepts in a request) levels cross-language
queries perform well in relation to monolingual queries. This suggests that
conjunction disambiguates cross-language queries. An extensive study
was made of
the relative importance of the concepts of requests. On the basis of the
classification data of request concepts it was shown how the order
of facets in a
query affects cross-language as well as monolingual queries.},
journal = {Inf. Retr.},
month = oct,
pages = {217–250},
numpages = {34},
keywords = {cross-language information retrieval, dictionary-based translation, Boolean conjuction, concept-based queries}
}

@article{10.1023/A:1009931404333,
author = {Boughanem, M. and Chrisment, C. and Tamine, L.},
title = {Genetic Approach to Query Space Exploration},
year = {1999},
issue_date = {October 1999},
publisher = {Kluwer Academic Publishers},
address = {USA},
volume = {1},
number = {3},
issn = {1386-4564},
url = {https://doi.org/10.1023/A:1009931404333},
doi = {10.1023/A:1009931404333},
abstract = {This paper describes a genetic algorithm approach for
intelligent information retrieval. The goal is to find an optimal set
of documents which best matches the user‘s needs by exploring and
exploiting the document space. More precisely, we define a specific
genetic algorithm for information retrieval based on knowledge based
operators and guided by a heuristic for relevance multi-modality
problem solving. Experiments with TREC-6 French data and queries
show the effectiveness of our approach.},
journal = {Inf. Retr.},
month = oct,
pages = {175–192},
numpages = {18},
keywords = {information retrieval, relevance feedback, genetic algorithm}
}

@article{10.1023/A:1009990414324,
author = {Kantor, Paul and Robertson, Steve},
title = {Editorial},
year = {1999},
issue_date = {1999},
publisher = {Kluwer Academic Publishers},
address = {USA},
volume = {1},
number = {1–2},
issn = {1386-4564},
url = {https://doi.org/10.1023/A:1009990414324},
doi = {10.102

license: Unlicense

repository: goyalkaraniit/acm-cr

repo_name: goyalkaraniit/acm-cr