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Word Sense Discrimination by Clustering Contexts in Vector and Similarity Spaces(2004), pp. 41-48.
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AbstractThis paper systematically compares unsupervised word sense discrimination techniques that cluster instances of a target word that occur in raw text using both vector and similarity spaces. The context of each instance is represented as a vector in a high dimensional feature space. Discrimination is achieved by clustering these context vectors directly in vector space and also by finding pairwise similarities among the vectors and then clustering in similarity space. We employ two...
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