Measuring Semantic Similarity and Relatedness with Distributional and Knowledge- based Approaches

Abstract

This paper provides a survey of different techniques for measuring semantic similarity and relatedness of word pairs. This covers both knowledge-based approaches exploiting taxonomies like WordNet, and corpus-based approaches which rely on distributional statistics. We introduce these techniques, provide evaluations of their result performance, and discuss their merits and shortcomings. A special focus is on word embeddings, a new technique which recently became popular with the AI community. While word embeddings are not fully understood yet, they show promising results for similarity tasks, and may also be suitable for capturing significantly more complex features like relational similarity.