Including category information as supplements in latent semantic analysis of Hindi documents
Vishnu Vardhan Bulusu, Vijayapal Reddy Panuganti, Karthik Krishnamurthi · International Journal of Computational Science and Engineering · 2017
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.