Ebook: Language Modeling for Information Retrieval

A statisticallanguage model, or more simply a language model, is a prob­ abilistic mechanism for generating text. Such adefinition is general enough to include an endless variety of schemes. However, a distinction should be made between generative models, which can in principle be used to synthesize artificial text, and discriminative techniques to classify text into predefined cat­ egories. The first statisticallanguage modeler was Claude Shannon. In exploring the application of his newly founded theory of information to human language, Shannon considered language as a statistical source, and measured how weH simple n-gram models predicted or, equivalently, compressed natural text. To do this, he estimated the entropy of English through experiments with human subjects, and also estimated the cross-entropy of the n-gram models on natural 1 text. The ability of language models to be quantitatively evaluated in tbis way is one of their important virtues. Of course, estimating the true entropy of language is an elusive goal, aiming at many moving targets, since language is so varied and evolves so quickly. Yet fifty years after Shannon's study, language models remain, by all measures, far from the Shannon entropy liInit in terms of their predictive power. However, tbis has not kept them from being useful for a variety of text processing tasks, and moreover can be viewed as encouragement that there is still great room for improvement in statisticallanguage modeling.

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This book contains the first collection of papers addressing recent developments in the design of information retrieval systems using language modeling techniques. Language modeling approaches are used in a variety of other language technologies, such as speech recognition and machine translation, and the book shows that applications such as Web search, cross-lingual search, filtering, and summarization can be described in the same formal framework. The book is intended primarily for researchers and advanced graduate students working in the language technologies areas of computer science or information science.

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This book contains the first collection of papers addressing recent developments in the design of information retrieval systems using language modeling techniques. Language modeling approaches are used in a variety of other language technologies, such as speech recognition and machine translation, and the book shows that applications such as Web search, cross-lingual search, filtering, and summarization can be described in the same formal framework. The book is intended primarily for researchers and advanced graduate students working in the language technologies areas of computer science or information science.
Content:
Front Matter....Pages i-xiii
Probabilistic Relevance Models Based on Document and Query Generation....Pages 1-10
Relevance Models in Information Retrieval....Pages 11-56
Language Modeling and Relevance....Pages 57-71
Contributions of Language Modeling to the Theory and Practice of Information Retrieval....Pages 73-93
Language Models for Topic Tracking....Pages 95-123
A Probabilistic Approach to Term Translation for Cross-Lingual Retrieval....Pages 125-140
Using Compression-Based Language Models for Text Categorization....Pages 141-165
Applications of Score Distributions in Information Retrieval....Pages 167-188
An Unbiased Generative Model for Setting Dissemination Thresholds....Pages 189-217
Language Modeling Experiments in Non-Extractive Summarization....Pages 219-244
Back Matter....Pages 245-245

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This book contains the first collection of papers addressing recent developments in the design of information retrieval systems using language modeling techniques. Language modeling approaches are used in a variety of other language technologies, such as speech recognition and machine translation, and the book shows that applications such as Web search, cross-lingual search, filtering, and summarization can be described in the same formal framework. The book is intended primarily for researchers and advanced graduate students working in the language technologies areas of computer science or information science.
Content:
Front Matter....Pages i-xiii
Probabilistic Relevance Models Based on Document and Query Generation....Pages 1-10
Relevance Models in Information Retrieval....Pages 11-56
Language Modeling and Relevance....Pages 57-71
Contributions of Language Modeling to the Theory and Practice of Information Retrieval....Pages 73-93
Language Models for Topic Tracking....Pages 95-123
A Probabilistic Approach to Term Translation for Cross-Lingual Retrieval....Pages 125-140
Using Compression-Based Language Models for Text Categorization....Pages 141-165
Applications of Score Distributions in Information Retrieval....Pages 167-188
An Unbiased Generative Model for Setting Dissemination Thresholds....Pages 189-217
Language Modeling Experiments in Non-Extractive Summarization....Pages 219-244
Back Matter....Pages 245-245
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