Online from: 1977
Subject Area: Library and Information Studies
Options: To add Favourites and Table of Contents Alerts please take a Emerald profile
|Title:||Term suggestion with similarity measure based on semantic analysis techniques in query logs|
|Author(s):||Lin-Chih Chen, (Department of Information Management, National Dong Hwa University, Hualien, Taiwan, ROC)|
|Citation:||Lin-Chih Chen, (2011) "Term suggestion with similarity measure based on semantic analysis techniques in query logs", Online Information Review, Vol. 35 Iss: 1, pp.9 - 33|
|Keywords:||Data management, Information retrieval, Query languages, Search engines, Semantics|
|Article type:||Research paper|
|DOI:||10.1108/14684521111113560 (Permanent URL)|
|Publisher:||Emerald Group Publishing Limited|
|Acknowledgements:||The author would like to thank the anonymous reviewers of the paper for their constructive comments that helped to improve the paper in several ways. This work was supported in part by the National Science Council, Taiwan under Grant NSC 97-2221-E-259-026.|
Purpose – Term suggestion is a very useful information retrieval technique that tries to suggest relevant terms for users' queries, to help advertisers find more appropriate terms relevant to their target market. This paper aims to focus on the problem of using several semantic analysis methods to implement a term suggestion system.
Design/methodology/approach – Three semantic analysis techniques are adopted – latent semantic indexing (LSI), probabilistic latent semantic indexing (PLSI), and a keyword relationship graph (KRG) – to implement a term suggestion system.
Findings – This paper shows that using multiple semantic analysis techniques can give significant performance improvements.
Research limitations/implications – The suggested terms returned from the system may be out of date, since the system uses a batch processing mode to update the training parameter.
Originality/value – The paper shows that the benefit of the techniques is to overcome the problems of synonymy and polysemy over the information retrieval field, by using a vector space model. Moreover, an intelligent stopping strategy is proposed to save the required number of iterations for probabilistic latent semantic indexing.
To purchase this item please login or register.
Complete and print this form to request this document from your librarian