Term representation with Generalized Latent Semantic Analysis
Irina Vladimirovna Matveeva, Gina‐Anne Levow, Ayman Farahat, Christiaan Royer · Amsterdam studies in the theory and history of linguistic science. Series 4, Current issues in linguistic theory · 2007
Document indexing and representation of termdocument relations are very important issues for document clustering and retrieval. In this paper, we present Generalized Latent Semantic Analysis as a framework for computing semantically motivated term and document vectors. Our focus on term vectors is motivated by the recent success of co-occurrence based measures of semantic similarity obtained from very large corpora. Our experiments demonstrate that GLSA term vectors efficiently capture semantic relations between terms and outperform related approaches on the synonymy test.