Paper: Near-synonym Lexical Choice in Latent Semantic Space

ACL ID C10-1133
Title Near-synonym Lexical Choice in Latent Semantic Space
Venue International Conference on Computational Linguistics
Session Main Conference
Year 2010

We explore the near-synonym lexical choice problem using a novel representa- tion of near-synonyms and their contexts in the latent semantic space. In contrast to traditional latent semantic analysis (LSA), our model is built on the lexical level of co-occurrence, which has been empir- ically proven to be effective in provid- ing higher dimensional information on the subtle differences among near-synonyms. By employing supervised learning on the latent features, our system achieves an ac- curacy of 74.5% in a “fill-in-the-blank” task. The improvement over the current state-of-the-art is statistically significant. We also formalize the notion of subtlety through its relation to semantic space di- mensionality. Using this formalization and our learning models, several of our intuitions a...