An Adapted Lesk Algorithm for Word Sense Disambiguation Using WordNet
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In this paper we describe an unsupervised WordNet-based Word Sense Disambiguation system, which participated (as UMND1) in the SemEval-2007 Coarse-grained English Lexical Sample task. The system disambiguates a target word by using WordNet-based measures of semantic relatedness to find the sense of the word that is semantically most strongly related to the senses of the words in the context of the target word. We briefly describe this system, the configuration options used for the task, and present some analysis of the results.