Query Segmentation Based on Eigenspace Similarity

Paper: Query Segmentation Based on Eigenspace Similarity

ACL ID	P09-2047
Title	Query Segmentation Based on Eigenspace Similarity
Venue	Annual Meeting of the Association of Computational Linguistics
Session	Short Paper
Year	2009
Authors	Chao Zhang (University of Electronic Science and Technology, Chengdu China; National University of Singapore, Singapore) Nan Sun (University of Electronic Science and Technology, Chengdu China) Xia Hu (National University of Singapore, Singapore) Tingzhu Huang Tat-Seng Chua

Query segmentation is essential to query processing. It aims to tokenize query words into several semantic segments and help the search engine to improve the precision of retrieval. In this paper, we present a novel unsupervised learning ap- proach to query segmentation based on principal eigenspace similarity of query- word-frequency matrix derived from web statistics. Experimental results show that our approach could achieve superior per- formance of 35.8% and 17.7% in F- measure over the two baselines respec- tively, i.e. MI (Mutual Information) ap- proach and EM optimization approach.

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