Using LSI and its variants in Text Classification

Abstract

Latent Semantic Indexing (LSI), a well known technique in Information Retrieval has been partially successful in text retrieval and no major breakthrough has been achieved in text classification as yet. A significant step forward in this regard was made by Hofmann[3], who presented the probabilistic LSI (PLSI) model, as an alternative to LSI. If we wish to consider exchangeable representations for documents and words, PLSI is not successful which further led to the Latent Dirichlet Allocation (LDA) model [4]. A new local Latent Semantic Indexing method has been proposed by some authors called “Local Relevancy Ladder-Weighted LSI” (LRLW-LSI) to improve text classification [5]. In this paper we study LSI and its variants in detail , analyze the role played by them in text classification and conclude with future directions in this area.