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Latent Semantic Analysis for Text Mining and Beyond
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Author(s): Anne Kao (Boeing Research & Technology, USA), Steve Poteet (Boeing Research & Technology, USA), Jason Wu (Boeing Research & Technology, USA), William Ferng (Boeing Research & Technology, USA), Rod Tjoelker (Boeing Research & Technology, USA)and Lesley Quach (Boeing Research & Technology, USA)
Copyright: 2012
Pages: 28
Source title:
Intelligent Multimedia Databases and Information Retrieval: Advancing Applications and Technologies
Source Author(s)/Editor(s): Li Yan (Northeastern University, China)and Zongmin Ma (Northeastern University, China)
DOI: 10.4018/978-1-61350-126-9.ch015
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Abstract
Latent Semantic Analysis (LSA) or Latent Semantic Indexing (LSI), when applied to information retrieval, has been a major analysis approach in text mining. It is an extension of the vector space method in information retrieval, representing documents as numerical vectors but using a more sophisticated mathematical approach to characterize the essential features of the documents and reduce the number of features in the search space. This chapter summarizes several major approaches to this dimensionality reduction, each of which has strengths and weaknesses, and it describes recent breakthroughs and advances. It shows how the constructs and products of LSA applications can be made user-interpretable and reviews applications of LSA beyond information retrieval, in particular, to text information visualization. While the major application of LSA is for text mining, it is also highly applicable to cross-language information retrieval, Web mining, and analysis of text transcribed from speech and textual information in video.
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