A survey of semantic relatedness evaluation datasets and procedures

Abstract

Semantic relatedness between words is a core concept in natural language processing. While countless approaches have been proposed, measuring which one works best is still a challenging task. Thus, in this article, we give a comprehensive overview of the evaluation protocols and datasets for semantic relatedness covering both intrinsic and extrinsic approaches. One the intrinsic side, we give an overview of evaluation datasets covering more than 100 datasets in 20 different languages from a wide range of domains. To provide researchers with better guidance for selecting suitable dataset or even building new and better ones, we describe also the construction and annotation process of the datasets. We also shortly describe the evaluation metrics most frequently used for intrinsic evaluation. As for the extrinsic side, several applications involving semantic relatedness measures are detailed through recent research works and by explaining the benefit brought by the measures.