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  電子郵件訊息： 我想你可能會對此資料感興趣，資料位於 http://www.worldcat.org/oclc/608352102 題名： A resource-light approach to morpho-syntactic tagging 作者： Anna Feldman; Jirka Hana 出版商： Amsterdam ; New York, NY : Rodopi, 2010. ISBN/ISSN: 9789042027695 904202769X 9042027681 9789042027688 OCLC:608352102

  
  

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A resource-light approach to morpho-syntactic tagging

作者：	Anna Feldman; Jirka Hana
出版商：	Amsterdam ; New York, NY : Rodopi, 2010.
叢書：	Language and computers, no. 70.
版本/格式：	電子書 : 文獻 : 英語所有版本和格式的總覽
提要：	While supervised corpus-based methods are highly accurate for different NLP tasks, including morphological tagging, they are difficult to port to other languages because they require resources that are expensive to create. As a result, many languages have no realistic prospect for morpho-syntactic annotation in the foreseeable future. The method presented in this book aims to overcome this problem by significantly limiting the necessary data and instead extrapolating the relevant information from another, related language. The approach has been tested on Catalan, Portuguese, and Russian. Although these languages are only relatively resource-poor, the same method can be in principle applied to any inflected language, as long as there is an annotated corpus of a related language available. Time needed for adjusting the system to a new language constitutes a fraction of the time needed for systems with extensive, manually created resources: days instead of years. This book touches upon a number of topics: typology, morphology, corpus linguistics, contrastive linguistics, linguistic annotation, computational linguistics and Natural Language Processing (NLP). Researchers and students who are interested in these scientific areas as well as in cross-lingual studies and applications will greatly benefit from this work. Scholars and practitioners in computer science and linguistics are the prospective readers of this book.  再讀一些...
評定級別：	(尚未評分) 0 附有評論 - 成爲第一個。
主題	Grammar, Comparative and general -- Morphosyntax. Computational linguistics. Language transfer (Language learning) 所有主題的總覽
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