Word Clustering Based on Un-LP Algorithm
Jiguang Liang, Xiaofei Zhou, Yue Hu, Li Guo, Shuo Bai · 2014
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.