文章基本信息

标题：Measuring Semantic Similarity and Relatedness with Distributional and Knowledge-based Approaches
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作者：Christoph LOFI
期刊名称：Information and Media Technologies
电子版ISSN：1881-0896
出版年度：2015
卷号：10
期号：3
页码：493-501
DOI：10.11185/imt.10.493
出版社：Information and Media Technologies Editorial Board
摘要：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.