Semantic Similarity Measure Using Relational and Latent Topic Features

Computing the semantic similarity between words is one of the key challenges in many language-based applications. Previous work tends to use the contextual information of words to disclose the degree of their similarity. In this paper, we consider the relationships between words in local contexts as well as latent topic information of words to propose a new distributed representation of words for semantic similarity measure. The method models meanings of a word as high dimensional Vector Space Models (VSMs) which combine relational features in word local contexts and its latent topic features in the global sense. Our experimental results on popular semantic similarity datasets show significant improvement of correlation scores with human judgements in comparison with other methods using purely plain texts.