Word-Sense Disambiguation for Machine Translation

Proceedings of Human Language Technology Conference and Conference on Empirical Methods in Natural Language Processing (HLT/EMNLP), pages 771–778, Vancouver, October 2005. c⃝2005 Association for Computational Linguistics Word-Sense Disambiguation for Machine Translation David Vickrey Luke Biewald Marc Teyssier Daphne Koller Department of Computer Science Stanford University Stanford, CA 94305-9010 {dvickrey,lukeb,teyssier,koller}@cs.stanford.edu Abstract In word sense disambiguation, a system attempts to determine the sense of a word from contextual fea- tures. Major barriers to building a high-performing word sense disambiguation system include the dif- ficulty of labeling data for this task and of pre- dicting fine-grained sense distinctions. These is- sues stem partly from the fact that the task is be- ing treated in isolation from possible uses of au- tomatically disambiguated data. In this paper, we consider the related task of word translation, where we wish to determine the correct translation of a word from context. We can use parallel language corpora as a large supply of partially labeled data for this task. We present algorithms for solving the word translation problem and demonstrate a signif- icant improvement over a baseline system. We then show that the word-translation system can be used to improve performance on a simplified machine- translation task and can effectively and accurately prune the set of candidate translations for a word. 1 Introduction The problem of distinguishing between multiple possible senses of a word is an important subtask in many NLP applications. However, despite its con- ceptual simplicity, and its obvious formulation as a standard classification problem, achieving high lev- els of performance on this task has been a remark- ably elusive goal. In its standard formulation, the disambiguation task is specified via an ontology defining the dif- ferent senses of ambiguous words. In the Sense- val competition, for example, WordNet (Fellbaum, 1998) is used to define this ontology. However, on- tologies such as WordNet are not ideally suited to the task of word-sense disambiguation. In many cases, WordNet is overly “specific”, defining senses which are very similar and hard to distinguish. For example, there are seven definitions of “respect” as a noun (including closely related senses such as “an attitude of admiration or esteem” and “a feel- ing of friendship and esteem”); there are even more when the verb definitions are included as well. Such closely related senses pose a challenge both for auto- matic disambiguation and hand labeling. Moreover, the use of a very fine-grained set of senses, most of which are quite rare in practice, makes it very diffi- cult to obtain sufficient amounts of training data. These issues are clearly reflected in the perfor- mance of current word-sense disambiguation sys- tems. When given a large amount of training data for a particular word with reasonably clear sense distinctions, existing systems perform fairly well. However, for the “all-words” task, where all am- biguous words from a test corpus must be disam- biguated, it has so far proved difficult to perform sig- nificantly better than the baseline heuristic of choos- ing the most common sense for …

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