Determining the Context of Text Using Augmented Latent Semantic Indexing
Document Type
Article
Publication Date
12-1-2007
Department
Computing
School
Computing Sciences and Computer Engineering
Abstract
Latent semantic analysis has been used for several years to improve the performance of document library searches. We show that latent semantic analysis, augmented with a Part-of-Speech Tagger, may be an effective algorithm for classifying a textual document as well. Using Brille's Part-of-Speech Tagger, we truncate the singular value decomposition used in latent semantic analysis to reduce the size of the word-frequency matrix. This method is then tested on a toy problem, and has shown to increase search accuracy. We then relate these results to natural language processing and show that latent semantic analysis can be combined with context free grammars to infer semantic meaning from natural language. English is the natural language currently being used.
Publication Title
Journal of the American Society for Information Science and Technology
Volume
58
Issue
14
First Page
2197
Last Page
2204
Recommended Citation
Rishel, T.,
Perkins, L. A.,
Yenduri, S.,
Zand, F.
(2007). Determining the Context of Text Using Augmented Latent Semantic Indexing. Journal of the American Society for Information Science and Technology, 58(14), 2197-2204.
Available at: https://aquila.usm.edu/fac_pubs/1863