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Noun Homograph Disambiguation Using Local Context in Large Text Corpora

This paper describes an accurate, relatively inexpensive method for the disambiguation of noun homographs using large text corpora. The algorithm checks the context surrounding the target noun against that of previously observed instances and chooses the sense for which the most evidence is found, where evidence consists of a set of orthographic, syntactic, and lexical features. Because the sense distinctions made are coarse, the disambiguation can be accomplished without the expense of knowledge bases or inference mechanisms. An implementation of the algorithm is described which, starting with a small set of hand-labeled instances, improves its results automatically via unsupervised training. The approach is compared to other attempts at homograph disambiguation using both machine readable dictionaries and unrestricted text and the use of training instances is determined to be a crucial di erence.