Understanding Latent Semantic Indexing: A Topological Structure Analysis Using Q-Analysis Method

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 paper, 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 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 query expansion method. Experimental results show that our method can ...

• Type: Conference
• Year: 2007
• Where: IKE
• Authors: Dandan Li, Chungping Kwong
• Keywords: IKE 2007, Information Retrieval, Information Technology, Latent Semantic Indexing, Topological Structure