Document (#35428)

Author

Li, D.
Kwong, C.-P.

Title

Understanding latent semantic indexing : a topological structure analysis using Q-analysis

Source

Journal of the American Society for Information Science and Technology. 61(2010) no.3, S.592-608

Year

2010

Abstract

The method of latent semantic indexing (LSI) is well-known for tackling the synonymy and polysemy problems in information retrieval; however, its performance can be very different for various datasets, and the questions of what characteristics of a dataset and why these characteristics contribute to this difference have not been fully understood. In this article, we propose that the mathematical structure of simplexes can be attached to a term-document matrix in the vector space model (VSM) for information retrieval. The Q-analysis devised by R.H. Atkin ([1974]) may then be applied to effect an analysis of the topological structure of the simplexes and their corresponding dataset. Experimental results of this analysis reveal that there is a correlation between the effectiveness of LSI and the topological structure of the dataset. By using the information obtained from the topological analysis, we develop a new method to explore the semantic information in a dataset. Experimental results show that our method can enhance the performance of VSM for datasets over which LSI is not effective.

Object

Latent Semantic Indexing
Q-analysis

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4. Zhuge, H.; Zhang, J.: Topological centrality and its e-Science applications (2010) 0.28

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5. Rishel, T.; Perkins, L.A.; Yenduri, S.; Zand, F.: Determining the context of text using augmented latent semantic indexing (2007) 0.26

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