Word-Sense Disambiguation Using Statistical Methods

WORD-SENSE DISAMBIGUATION USING STATISTICAL METHODS Peter F. Brown, Stephen A. Della Pietra, Vincent J. Della Pietra, and Robert L. Mercer IBM Thomas J. Watson Research Center P.O. Box 704 Yorktown Heights, NY 10598 ABSTRACT We describe a statistical technique for assign- ing senses to words. An instance of a word is as- signed a sense by asking a question about the con- text in which the word appears. The question is constructed to have high mutual information with the translation of that instance in another lan- guage. When we incorporated this method of as- signing senses into our statistical machine transla- tion system, the error rate of the system decreased by thirteen percent. INTRODUCTION An alluring aspect of the statistical ~p- proach to machine translation rejuvenated by Brown et al. [Brown et al., 1988, Brown et al., 1990] is the systematic framework it provides for attacking the problem of lexical disam- biguation. For example, the system they de- scribe translates the French sentence Je vais prendre la ddcision as I will make the decision, correctly interpreting prendre as make. The statistical translation model, which supplies English translations of French words, prefers the more common translation take, bnt the trigram language model recognizes that the three-word sequence make the decision, is much more probable than take the decision.. The system is not always so successfifl. It incorrectly renders Je vais prendre ma propre ddcision as 1 will take my own decision. The language model does not realize that take my own decision is improbable because take and decision no longer fall within a single trigram. Errors such as this are common because the statistical models only capture local phe- nomena; if the context necessary to determine a translation falls outside the scope of the models, the word is likely to be translated in- correctly, t[owever, if the relevant context is encoded locally, the word should be translated correctly. We can achieve this within the tra- ditional paradigm of analysis, transfer, and synthesis by incorporating into the analysis phase a sense-disambiguation component that assigns sense labels to French words. If pren- dre is labeled with one sense in the context of ddcision but with a different sense in other contexts, then the translation model will learn front trMning data that the first sense usually translates to make, whereas the other sense usuMly translates to take. Previous efforts a.t algorithmic disambigua- tion of word senses [Lesk, 1986, White, 1988, Ide and V6ronis, 1990] have concentrated on information that can be extracted from elec- tronic dictionaries, and focus, therefore, on senses as determined by those dictionaries. llere, in contrast, we present a procedure for constructing a sense-disambiguation compo- nent that labels words so as to elucidate their translations in another language. We are con- 264 The proposal Les propositions will not / ne seront pas now be implemented mises en application maintenant Figure 1: Alignment Example cerned about senses as they occur in a dic- tionary only to the extent that those senses are …

Content truncated to 500 words