Word Clustering Based on Un-LP Algorithm
نویسندگان
چکیده
Word clustering which generalizes specific features cluster words in the same syntactic or semantic categories into a group. It is an effective approach to reduce feature dimensionality and feature sparseness which are clearly useful for many NLP applications. This paper proposes an unsupervised label propagation algorithm (Un-LP) for word clustering which uses multi-exemplars to represent a cluster. Experiments on a synthetic 2D dataset show the strong ability of selfcorrecting of the proposed algorithm. Besides, the experimental results on 20NG demonstrate that our algorithm outperforms the conventional cluster algorithms.
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