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Titlebook: Language Modeling for Information Retrieval; W. Bruce Croft (Distinguished Professor),John Laff Book 2003 Springer Science+Business Media

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发表于 2025-3-21 18:28:28 | 显示全部楼层 |阅读模式
书目名称Language Modeling for Information Retrieval
编辑W. Bruce Croft (Distinguished Professor),John Laff
视频video
丛书名称The Information Retrieval Series
图书封面Titlebook: Language Modeling for Information Retrieval;  W. Bruce Croft (Distinguished Professor),John Laff Book 2003 Springer Science+Business Media
描述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,
出版日期Book 2003
关键词DOM; Performance; Text; cognition; database; filtering; machine translation; speech recognition
版次1
doihttps://doi.org/10.1007/978-94-017-0171-6
isbn_softcover978-90-481-6263-5
isbn_ebook978-94-017-0171-6Series ISSN 1871-7500 Series E-ISSN 2730-6836
issn_series 1871-7500
copyrightSpringer Science+Business Media Dordrecht 2003
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The Information Retrieval Serieshttp://image.papertrans.cn/l/image/580946.jpg
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978-90-481-6263-5Springer Science+Business Media Dordrecht 2003
发表于 2025-3-22 06:25:34 | 显示全部楼层
Language Modeling for Information Retrieval978-94-017-0171-6Series ISSN 1871-7500 Series E-ISSN 2730-6836
发表于 2025-3-22 11:21:58 | 显示全部楼层
https://doi.org/10.1007/978-94-017-0171-6DOM; Performance; Text; cognition; database; filtering; machine translation; speech recognition
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A Probabilistic Approach to Term Translation for Cross-Lingual Retrieval,ormance level. The third aspect is to describe a technique that can potentially reduce the cost of manually creating a parallel corpus. Such a technique will be useful for language pairs with no or little parallel text.
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Using Compression-Based Language Models for Text Categorization,rted. These tasks in increasing order of difficulty are language and dialect identification, authorship ascription, genre classification and topic classification. The results show that text categorization based on PPM is extremely effective for language and dialect identification, and for authorship
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Book 2003itatively 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,
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