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Combining Orthogonal Monolingual and Multilingual Sources of Evidence for All Words WSD

Guo, Weiwei; Diab, Mona

Word Sense Disambiguation remains one of the most complex problems facing computational linguists to date. In this paper we present a system that combines evidence from a monolingual WSD system together with that from a multilingual WSD system to yield state of the art performance on standard All-Words data sets. The monolingual system is based on a modification of the graph based state of the art algorithm In-Degree. The multilingual system is an improvement over an AllWords unsupervised approach, SALAAM. SALAAM exploits multilingual evidence as a means of disambiguation. In this paper, we present modifications to both of the original approaches and then their combination. We finally report the highest results obtained to date on the SENSEVAL 2 standard data set using an unsupervised method, we achieve an overall F measure of 64.58 using a voting scheme.

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Computer Science
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April 29, 2013