Query segmentation based on eigenspace similarity

Authors:
Chao Zhang;Nan Sun;Xia Hu;Tingzhu Huang;Tat-Seng Chua
Affiliations:
University of Electronic Science, and Technology of China, Chengdu, P.R. China and National University of Singapore, Singapore;National University of Singapore, Singapore;National University of Singapore, Singapore;University of Electronic Science, and Technology of China, Chengdu, P.R. China;National University of Singapore, Singapore
Venue:
ACLShort '09 Proceedings of the ACL-IJCNLP 2009 Conference Short Papers
Year:
2009

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Abstract

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 approach 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 performance of 35.8% and 17.7% in F-measure over the two baselines respectively, i.e. MI (Mutual Information) approach and EM optimization approach.