Similarity Measures based on Latent Dirichlet Allocation

Nobal B. Niraula

Abstract:

We present in this paper the results of our investigation on semantic similarity measures at word- and sentence-level based on two fully-automated approaches to deriving meaning from large corpora: Latent Dirichlet Allocation, a probabilistic approach, and Latent Semantic Analysis, an algebraic approach. The focus is on similarity measures based on Latent Dirichlet Allocation, due to its novelty aspects, while the Latent Semantic Analysis measures are used for comparison purposes. We explore two types of measures based on Latent Dirichlet Allocation: measures based on distances between probability distribution that can be applied directly to larger texts such as sentences and a word-to-word similarity measure that is then expanded to work at sentence-level. We present results using paraphrase identification data
in the Microsoft Research Paraphrase corpus.