Please use this identifier to cite or link to this item: http://scholarbank.nus.edu.sg/handle/10635/40708
Title: Query segmentation based on eigenspace similarity
Authors: Zhang, C.
Sun, N. 
Hu, X.
Huang, T.
Chua, T.-S. 
Issue Date: 2009
Source: Zhang, C.,Sun, N.,Hu, X.,Huang, T.,Chua, T.-S. (2009). Query segmentation based on eigenspace similarity. ACL-IJCNLP 2009 - Joint Conf. of the 47th Annual Meeting of the Association for Computational Linguistics and 4th Int. Joint Conf. on Natural Language Processing of the AFNLP, Proceedings of the Conf. : 185-188. ScholarBank@NUS Repository.
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. © 2009 ACL and AFNLP.
Source Title: ACL-IJCNLP 2009 - Joint Conf. of the 47th Annual Meeting of the Association for Computational Linguistics and 4th Int. Joint Conf. on Natural Language Processing of the AFNLP, Proceedings of the Conf.
URI: http://scholarbank.nus.edu.sg/handle/10635/40708
ISBN: 9781617382581
Appears in Collections:Staff Publications

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