Query Segmentation Based on Eigenspace Similarity

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 queryword-frequency matrix derived from web statistics. Experimental results show that our approach could achieve superior performance of 35.8% and 17.7% in Fmeasure over the two baselines respectively, i.e. MI (Mutual Information) approach and EM optimization approach.

• Type: Journal
• Year: 2009
• Where: ACL
• Authors: Chao Zhang, Nan Sun, Xia Hu, Tingzhu Huang, Tat-Seng Chua
• Keywords: ACL 2009, Computational Linguistics, Principal Eigenspace Similarity, Query Processing, Query Segmentation