Exploiting Parallel Texts to Produce a Multilingual Sense Tagged Corpus for Word Sense Disambiguation
نویسندگان
چکیده
We describe an approach to the automatic creation of a sense tagged corpus intended to train a word sense disambiguation (WSD) system for English-Portuguese machine translation. The approach uses parallel corpora, translation dictionaries and a set of straightforward heuristics. In an evaluation with nine corpora containing 10 ambiguous verbs, the approach achieved an average precision of 94%, compared with 58% when a state of the art statistical alignment tool was used. The resulting corpus consists of 113,802 instances tagged with the senses (i.e., translations) of the 10 verbs. Besides the word-sense tags, this corpus provides other useful information, such as POS-tags, and can be readily used as input to supervised machine learning algorithms in order to build WSD models for machine translation.
منابع مشابه
Crossing Parallel Corpora and Multilingual Lexical Databases for WSD
Word Sense Disambiguation (WSD) is the task of selecting the correct sense of a word in a context from a sense repository. Typically, WSD is approached as a supervised classification task to get state-of-the-art performance (e.g. [6]), and thus a large amount of sense-tagged examples for each sense of the word is needed, according to the word-expert approach. This requirement makes the supervis...
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