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34
A Probabilistic Model of Information Retrieval: Development and Status
, 1998
"... The paper combines a comprehensive account of the probabilistic model of retrieval with new systematic experiments on TREC Programme material. It presents the model from its foundations through its logical development to cover more aspects of retrieval data and a wider range of system functions. Eac ..."
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Cited by 263 (19 self)
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The paper combines a comprehensive account of the probabilistic model of retrieval with new systematic experiments on TREC Programme material. It presents the model from its foundations through its logical development to cover more aspects of retrieval data and a wider range of system functions. Each step in the argument is matched by comparative retrieval tests, to provide a single coherent account of a major line of research. The experiments demonstrate, for a large test collection, that the probabilistic model is effective and robust, and that it responds appropriately, with major improvements in performance, to key features of retrieval situations.
Information Retrieval Interaction
, 1992
"... this document, text or image about?' Gradually moving from the left to the right in Figure 3.1, different understandings of this concept evolve ..."
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Cited by 181 (6 self)
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this document, text or image about?' Gradually moving from the left to the right in Figure 3.1, different understandings of this concept evolve
Clustering User Queries of a Search Engine
, 2001
"... In order to increase retrieval precision, some new search engines provide manually verified answers to Frequently Asked Queries (FAQs). An underlying task is the identification of FAQs. This paper describes our attempt to cluster similar queries according to their contents as well as user logs. Our ..."
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Cited by 113 (3 self)
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In order to increase retrieval precision, some new search engines provide manually verified answers to Frequently Asked Queries (FAQs). An underlying task is the identification of FAQs. This paper describes our attempt to cluster similar queries according to their contents as well as user logs. Our preliminary results show that the resulting clusters provide useful information for FAQ identification.
Probabilistic Models in Information Retrieval
 The Computer Journal
, 1992
"... In this paper, an introduction and survey over probabilistic information retrieval (IR) is given. First, the basic concepts of this approach are described: the probability ranking principle shows that optimum retrieval quality can be achieved under certain assumptions; a conceptual model for IR alon ..."
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Cited by 103 (4 self)
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In this paper, an introduction and survey over probabilistic information retrieval (IR) is given. First, the basic concepts of this approach are described: the probability ranking principle shows that optimum retrieval quality can be achieved under certain assumptions; a conceptual model for IR along with the corresponding event space clarify the interpretation of the probabilistic parameters involved. For the estimation of these parameters, three different learning strategies are distinguished, namely queryrelated, documentrelated and descriptionrelated learning. As a representative for each of these strategies, a specific model is described. A new approach regards IR as uncertain inference; here, imaging is used as a new technique for estimating the probabilistic parameters, and probabilistic inference networks support more complex forms of inference. Finally, the more general problems of parameter estimation, query expansion and the development of models for advanced document representations are discussed.
A Probabilistic Learning Approach for Document Indexing
 ACM TRANSACTIONS ON INFORMATION SYSTEMS
, 1991
"... We describe a method for probabilistic document indexing using relevance feedback data that has been collected from a set of queries. Our approach is based on three new concepts: (1) Abstraction from specific terms and documents, which overcomes the restriction of limited relevance information fo ..."
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Cited by 93 (13 self)
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We describe a method for probabilistic document indexing using relevance feedback data that has been collected from a set of queries. Our approach is based on three new concepts: (1) Abstraction from specific terms and documents, which overcomes the restriction of limited relevance information for parameter estimation. (2) Flexibility of the representation, which allows the integration of new text analysis and knowledgebased methods in our approach as well as the consideration of document structures or different types of terms. (3) Probabilistic learning or classification methods for the estimation of the indexing weights making better use of the available relevance information. Our approach can be applied under restrictions that hold for real applications. We give experimental results for five test collections which show improvements over other indexing methods.
"Is This Document Relevant? ...Probably": A Survey of Probabilistic Models in Information Retrieval
, 2001
"... This article surveys probabilistic approaches to modeling information retrieval. The basic concepts of probabilistic approaches to information retrieval are outlined and the principles and assumptions upon which the approaches are based are presented. The various models proposed in the developmen ..."
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Cited by 63 (14 self)
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This article surveys probabilistic approaches to modeling information retrieval. The basic concepts of probabilistic approaches to information retrieval are outlined and the principles and assumptions upon which the approaches are based are presented. The various models proposed in the development of IR are described, classified, and compared using a common formalism. New approaches that constitute the basis of future research are described
A Risk Minimization Framework for Information Retrieval
 IN PROCEEDINGS OF THE ACM SIGIR 2003 WORKSHOP ON MATHEMATICAL/FORMAL METHODS IN IR. ACM
, 2003
"... This paper presents a novel probabilistic information retrieval framework in which the retrieval problem is formally treated as a statistical decision problem. In this framework, queries and documents are modeled using statistical language models (i.e., probabilistic models of text), user preference ..."
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Cited by 47 (1 self)
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This paper presents a novel probabilistic information retrieval framework in which the retrieval problem is formally treated as a statistical decision problem. In this framework, queries and documents are modeled using statistical language models (i.e., probabilistic models of text), user preferences are modeled through loss functions, and retrieval is cast as a risk minimization problem. We discuss how this framework can unify existing retrieval models and accommodate the systematic development of new retrieval models. As an example of using the framework to model nontraditional retrieval problems, we derive new retrieval models for subtopic retrieval, which is concerned with retrieving documents to cover many different subtopics of a general query topic. These new models differ from traditional retrieval models in that they go beyond independent topical relevance.
Some inconsistencies and misidentified modelling assumptions in probabilistic information retrieval
 A CM Transactions on Information Systems
, 1995
"... Research in the probabilistic theory of information retrieval involves the construction of mathematical models based on statistical assumptions. One of the hazards inherent in this kind of theory construction is that the assumptions laid down maybe inconsmtent in unanticipated ways with the data to ..."
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Cited by 26 (0 self)
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Research in the probabilistic theory of information retrieval involves the construction of mathematical models based on statistical assumptions. One of the hazards inherent in this kind of theory construction is that the assumptions laid down maybe inconsmtent in unanticipated ways with the data to which they are applied. Another hazard is that the stated assumptions may not be those on which the derived modeling equations or resulting experiments are actually based. Both kinds of mistakes have been made m past research on probabihstic reformation retrieval. One consequence of these errors is that the statistical character of certain probabilistic IR models, including the socalled Binary Independence model, has been seriously misapprehended Categories and Subject Descriptors: H. 1.2 [Models and Principles]: User/Machine Systems;