Word sense disambiguation based on positional weighted context

Abstract

Word sense disambiguation (WSD) is a key factor in solving natural language processing problems. The purpose of WSD is to make computers automatically determine the specific meaning of a word in a specific context. In this regard, state-of-art studies have focussed on the co-occurrences of words to measure context similarity. However, a problem with these approaches is that they consider all the words within a certain range to have equal influence on the ambiguous word. In this paper, we propose a position-based algorithm for measuring context similarity. By assigning positional weights to context words, we compared the context similarity between a new instance and pre-labelled instances to determine the appropriate sense of the ambiguous word. Experiments on the Senseval-2 English lexical sample task showed that our algorithm can achieve good precision and recall. Even in a minimally supervised state, it performs well with few training instances.

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