Title :
A Latent Semantic Analysis Based Method of Getting the Category Attribute of Words
Author :
Jiang, Zongli ; Lu, Changdong
Author_Institution :
Lab. of Comput. Software & Theor., Beijing Univ. of Technol., Beijing
Abstract :
Current search engines have two problems, losing useful information and including useless information. These two problems are aroused by the keyword matching retrieval model, which is adopted by almost all search engines. We introduce the conception of category attribute of a word. According to the category attribute of a word, the useless results can be removed from the search results and the retrieval efficiency will be improved. A latent semantic analysis based method of getting the category attribute of the word is presented in this paper, which is proved to be effective by experiment. Latent semantic analysis is a method that can discover the underlying semantic relation between words and documents. Singular value decomposition is used in latent semantic analysis to analyze the words and documents and get the semantic relation finally.
Keywords :
information retrieval; search engines; singular value decomposition; keyword matching retrieval model; latent semantic analysis; retrieval efficiency; search engines; singular value decomposition; word category attribute retrieval; Information analysis; Information retrieval; Internet; Laboratories; Missiles; Search engines; Singular value decomposition; Software; Text categorization; Web pages; information retrieval; latent semantic analysis; search engine; text categorization;
Conference_Titel :
Electronic Computer Technology, 2009 International Conference on
Conference_Location :
Macau
Print_ISBN :
978-0-7695-3559-3
DOI :
10.1109/ICECT.2009.19