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Titlebook: Word Sense Disambiguation; Algorithms and Appli Eneko Agirre,Philip Edmonds Book 2007 Springer Science+Business Media B.V. 2007 algorithms.

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书目名称Word Sense Disambiguation
副标题Algorithms and Appli
编辑Eneko Agirre,Philip Edmonds
视频video
概述The first book to cover all aspects of word sense disambiguation.Contains contributions by leading researchers in the field
丛书名称Text, Speech and Language Technology
图书封面Titlebook: Word Sense Disambiguation; Algorithms and Appli Eneko Agirre,Philip Edmonds Book 2007 Springer Science+Business Media B.V. 2007 algorithms.
描述Graeme Hirst University of Toronto Of the many kinds of ambiguity in language, the two that have received the most attention in computational linguistics are those of word senses and those of syntactic structure, and the reasons for this are clear: these ambiguities are overt, their resolution is seemingly essential for any prac- cal application, and they seem to require a wide variety of methods and knowledge-sources with no pattern apparent in what any particular - stance requires. Right at the birth of artificial intelligence, in his 1950 paper “Computing machinery and intelligence”, Alan Turing saw the ability to understand language as an essential test of intelligence, and an essential test of l- guage understanding was an ability to disambiguate; his example involved deciding between the generic and specific readings of the phrase a winter’s day. The first generations of AI researchers found it easy to construct - amples of ambiguities whose resolution seemed to require vast knowledge and deep understanding of the world and complex inference on this kno- edge; for example, Pharmacists dispense with accuracy. The disambig- tion problem was, in a way, nothing less than the arti
出版日期Book 2007
关键词algorithms; artificial intelligence; computational linguistics; language; linguistics
版次1
doihttps://doi.org/10.1007/978-1-4020-4809-8
isbn_softcover978-1-4020-6870-6
isbn_ebook978-1-4020-4809-8Series ISSN 1386-291X Series E-ISSN 2542-9388
issn_series 1386-291X
copyrightSpringer Science+Business Media B.V. 2007
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Domain-Specific WSD,onaries or manually added to WordNet and similar semantic resources, the extraction of topic signatures through a combined use of a semantic resource and domain-specific corpora, and domainspecific tuning of semantic resources in a top-down or bottom-up fashion.
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Making Sense About Sense,riate goal for the computational WSD task. We argue that the level of sense-discrimination that natural language processing (NLP) needs corresponds roughly to homographs, though we discuss psycholinguistic evidence that there are broad sense divisions with some etymological derivation (i.e., non-hom
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Unsupervised Corpus-Based Methods for WSD,sources such as machine readable dictionaries, concept hierarchies, or sense-tagged text. They do not assign sense tags to words; rather, they discriminate among word meanings based on information found in unannotated corpora. This chapter reviews distributional approaches that rely on monolingual c
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