Title: Including category information as supplements in latent semantic analysis of Hindi documents

Authors: Karthik Krishnamurthi; Vijayapal Reddy Panuganti; Vishnu Vardhan Bulusu

Addresses: Department of Computer Science, Christ University, Bangalore, India ' Department of Computer Science and Engineering, Gokaraju Rangaraju Institute of Engineering and Technology, Hyderabad, India ' Department of Computer Science and Engineering, JNTUHCEJ, Karimnagar, India

Abstract: Latent semantic analysis (LSA) is a mathematical model that is used to capture the semantic structure of documents by using the correlations between the textual elements in them. LSA captures the semantic structure very well being independent of external sources of semantics. However, the model's performance increases when it is supplemented with extra information. The work presented in this paper is to modify the model to analyse word correlations in documents by considering the document category information as supplements in the process. This enhancement is called supplemented latent semantic analysis (SLSA). SLSA's performance is empirically evaluated in a document classification application by comparing the accuracies of classification against plain LSA for various term weighting schemes. An increment of 1.14%, 1.30% and 1.63% is observed in the classification accuracies when SLSA is compared with plain LSA for tf, idf and tfidf respectively in the initial term-bydocument matrix.

Keywords: dimensionality reduction; document classification; latent semantic analysis; LSA; semantic structure; singular value decomposition.

DOI: 10.1504/IJCSE.2017.085967

International Journal of Computational Science and Engineering, 2017 Vol.15 No.1/2, pp.138 - 145

Received: 24 Dec 2014
Accepted: 30 Sep 2015
Published online: 21 Aug 2017 *

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