Paper: Computing Term Translation Probabilities With Generalized Latent Semantic Analysis

ACL ID E06-2017
Title Computing Term Translation Probabilities With Generalized Latent Semantic Analysis
Venue Annual Meeting of The European Chapter of The Association of Computational Linguistics
Session System Demonstration
Year 2006
Authors

Term translation probabilities proved an effective method of semantic smoothing in the language modelling approach to infor- mation retrieval tasks. In this paper, we use Generalized Latent Semantic Analysis to compute semantically motivated term and document vectors. The normalized cosine similarity between the term vec- tors is used as term translation probabil- ity in the language modelling framework. Our experiments demonstrate that GLSA- based term translation probabilities cap- ture semantic relations between terms and improve performance on document classi- fication.