Augmentation of a term/document matrix with part-of-speech tags to improve accuracy of latent semantic analysis

Authors:
Tom Rishel;A. Louise Perkins;Sumanth Yenduri;Farnaz Zand;S. S. Iyengar
Affiliations:
Computer Science Dept., University of Southern Mississippi, Long Beach, MS;Computer Science Dept., University of Southern Mississippi, Long Beach, MS;Computer Science Dept., University of Southern Mississippi, Long Beach, MS;Computer Science Dept., University of Southern Mississippi, Long Beach, MS;Computer Science Dept., Louisiana State University, Baton Rouge, LA
Venue:
ACOS'06 Proceedings of the 5th WSEAS international conference on Applied computer science
Year:
2006

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Abstract

We consider the improvement in accuracy of latent semantic analysis when a part of speech tagger is used to augment a term/document matrix. We first construct an augmented term/document matrix as input into singular value decomposition (SVD). The singular values then serve as principal components for a cosine projection. The results show that the addition of POS tags can decrease ambiguities significantly.