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Pattern recognition: human and mechanical
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A probabilistic inference model for information retrieval
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Probabilistic models in information retrieval
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A comparison of text retrieval models
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Precision Weighting—An Effective Automatic Indexing Method
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Extended Boolean information retrieval
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Introduction to Modern Information Retrieval
Introduction to Modern Information Retrieval
The SMART Retrieval System—Experiments in Automatic Document Processing
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Retrieval of complex objects using a four-valued logic
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“Is this document relevant?…probably”: a survey of probabilistic models in information retrieval
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Discovery of similarity computations of search engines
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Anchor text mining for translation extraction of query terms
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Probabilistic models of information retrieval based on measuring the divergence from randomness
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VLDB '99 Proceedings of the 25th International Conference on Very Large Data Bases
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Proceedings of the 26th annual international ACM SIGIR conference on Research and development in informaion retrieval
From Retrieval Status Values to Probabilities of Relevance for Advanced IR Applications
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Gravitation-based model for information retrieval
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Expert Systems with Applications: An International Journal
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A risk minimization framework for information retrieval
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Expert Systems with Applications: An International Journal
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A retrieval method adaptively reducing user's subjective impression gap
Multimedia Tools and Applications
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IR Models: Foundations and Relationships
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This article examines and extends the logical models of information retrieval in the context of probability theory. The fundamental notions of term weights and relevance are given probabilistic interpretations. A unified framework is developed for modeling the retrieval process with probabilistic inference. This new approach provides a common conceptual and mathematical basis for many retrieval models, such as the Boolean, fuzzy set, vector space, and conventional probabilistic models. Within this framework, the underlying assumptions employed by each model are identified, and the inherent relationships between these models are analyzed. Although this article is mainly a theoretical analysis of probabilistic inference for information retrieval, practical methods for estimating the required probabilities are provided by simple examples.