This paper proposes a knowledge-based WSD (Word Sense Disambiguation) method derived from the Lesk algorithm. The proposed method considers an extension of the definition domain of the Lesk algorithm by creating a lexicon network from tagged lexicon glosses. We present several methods that adjust the lexicon network in order to better describe the natural language. Further, on the pre-processed lexicon network we build competence and definition semantic trees for each sense that will be used to compute costs of semantic similarity between words senses. For this purpose we use a WSD window limited to a phrase, and a similar reasoning for larger contexts. For testing we apply our methods to the recently WordNet tagged glosses.
In computational linguistics, the problem of word-sense disambiguation (WSD) is a difficult one and methods using a flat topology of the tokens are not very effective. One solution to this is to use a Part of Speech (POS) tagger before starting the WSD process. However, POS taggers show their limitations when high precision tagging is required or large texts are processed. This paper presents a technique to reduce the POS ambiguity using semantic information. As benchmarks we use as following standard WSD corpuses: Senseval2, Senseval3 and Semcor. Moreover, we tested our approach on WordNet semantically tagged glosses for English and on our own semantically tagged lexicon glosses for Romanian language.
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