Near-synonym Lexical Choice in Latent Semantic Space

We explore the near-synonym lexical choice problem using a novel representation 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 empirically proven to be effective in providing higher dimensional information on the subtle differences among near-synonyms. By employing supervised learning on the latent features, our system achieves an accuracy 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 dimensionality. Using this formalization and our learning models, several of our intuitions about subtlety, dimensionality, and context are quantified and empirically tested.