LREC 2000 - Papers

LREC 2000 2^nd International Conference on Language Resources & Evaluation

Conference Papers

Papers by paper title: A B C D E F G H I J K L M N O P Q R S T U V W X Y Z

Papers by ID number: 1-50, 51-100, 101-150, 151-200, 201-250, 251-300, 301-350, 351-377.

List of all papers and abstracts.

Previous Paper Next Paper

Title A Word Sense Disambiguation Method Using Bilingual Corpus

Authors Jie Zheng (Institute for Language and Speech Processing, Department of Automation, Tsinghua University, P.R.China, zhj@mail.au.tsinghua.edu.cn)
Yuhang Mao (Institute for Language and Speech Processing, Department of Automation, Tsinghua University, P.R.China)

Keywords Machine Translation, Natural Language Processing, Word Sense Tagging

Session Session WP3 - Multilingual Corpora

Abstract This paper proposes a word sense disambiguation (WSD) method using bilingual corpus in English-Chinese machine translation system. A mathematical model is constructed to disambiguate word in terms of context phrasal collocation. A rules learning algorithm is proposed, and an application algorithm of the learned rules is also provided, which can increase the recall ratio. Finally, an analysis is given by an experiment on the algorithm. Its application gives an increase of 10% in precision.

a">

Title	A Word Sense Disambiguation Method Using Bilingual Corpus
Authors	Jie Zheng (Institute for Language and Speech Processing, Department of Automation, Tsinghua University, P.R.China, zhj@mail.au.tsinghua.edu.cn) Yuhang Mao (Institute for Language and Speech Processing, Department of Automation, Tsinghua University, P.R.China)
Keywords	Machine Translation, Natural Language Processing, Word Sense Tagging
Session	Session WP3 - Multilingual Corpora
Abstract	This paper proposes a word sense disambiguation (WSD) method using bilingual corpus in English-Chinese machine translation system. A mathematical model is constructed to disambiguate word in terms of context phrasal collocation. A rules learning algorithm is proposed, and an application algorithm of the learned rules is also provided, which can increase the recall ratio. Finally, an analysis is given by an experiment on the algorithm. Its application gives an increase of 10% in precision.