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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.doi:10.11185/imt.10.493 fatcat:emuwh4pianaidnuib6fwd2jxlm