Using Bilingual Materials to Develop Word Sense Disambiguation Methods

Using Bilingual Materials to Develop Word Sense Disambiguation Methods (1992)

Cached

Download Links

[www.mt-archive.info]

by William A. Gale , Kenneth W. Church , David Yarowsky

Citations:

69 - 2 self

Versions

Version History

Metadata Version 1

User correction supplied by mph

Datum	Value	Source
TITLE	Using Bilingual Materials to Develop Word Sense Disambiguation Methods	user correction
AUTHOR NAME	William A. Gale	user correction
AUTHOR AFFIL	AT&T Bell Laboratories	user correction
AUTHOR ADDR	600 Mountain Avenue; P. O. Box 636; Murray Hill NJ, 07974-0636	user correction
AUTHOR NAME	Kenneth W. Church	user correction
AUTHOR AFFIL	AT&T Bell Laboratories	user correction
AUTHOR ADDR	600 Mountain Avenue; P. O. Box 636; Murray Hill NJ, 07974-0636	user correction
AUTHOR NAME	David Yarowsky	user correction
AUTHOR AFFIL	AT&T Bell Laboratories	user correction
AUTHOR ADDR	600 Mountain Avenue; P. O. Box 636; Murray Hill NJ, 07974-0636	user correction
ABSTRACT	Word sense disambiguation has been recognized as a major problem in natural language processing research for over forty years. Much of this work has been stymied by difficulties in acquiring appropriate lexical resources, such as semantic networks and annotated corpora. Following the suggestion in Brown et al. (1991a) and Dagan et al. (1991), we have achieved considerable progress recently by taking advantage of a new source of testing and training materials. Rather than depending on small amounts of hand-labeled text, we have been making use of relatively large amounts of parallel text, text such as the Canadian Hansards (parliamentary debates), which are available in two (or more) languages. The translation can often be used in lieu of hand-labeling. For example, consider the polysemous word sentence, which has two major senses: (1) a judicial sentence, and (2), a syntactic sentence. We can collect a number of sense (1) examples by extracting instances that are translated as peine, and we can collect a number of sense (2) examples by extracting instances that are translated as phrase. In this way, we have been able to acquire a considerable amount of testing and training material for developing and testing our disambiguation algorithms. The availability of this testing and training material has enabled us to develop quantitative disambiguation methods that achieve 90 % accuracy in discriminating between two very distinct senses of a noun such as	SVM HeaderParse 0.2
YEAR	1992	user correction
CITATIONS	43 found	ParsCit 1.0