Using Hidden Markov Random Fields to Combine Distributional and Pattern-Based Word Clustering

Word clustering is a conventional and important NLP task, and the literature has suggested two kinds of approaches to this problem. One is based on the distributional similarity and the other relies on the co-occurrence of two words in lexicosyntactic patterns. Although the two methods have been discussed separately, it is promising to combine them since they are complementary with each other. This paper proposes to integrate them using hidden Markov random fields and demonstrates its effectiveness through experiments.

• Type: Conference
• Year: 2008
• Where: COLING
• Authors: Nobuhiro Kaji, Masaru Kitsuregawa
• Keywords: COLING 2008, Computational Linguistics, Distributional Similarity, Important Nlp Task, Word Clustering