Evaluating WordNet-based Measures of Lexical Semantic Relatedness | Zendy

Alexander Budanitsky | Zendy; Graeme Hirst | Zendy

AI Assistant Blog Pricing

Home ZAIA Blog

Open Access

Evaluating WordNet-based Measures of Lexical Semantic Relatedness

Author(s) -

Alexander Budanitsky,

Graeme Hirst

Publication year - 2006

Publication title -

computational linguistics

Language(s) - English

Resource type - Journals

SCImago Journal Rank - 0.314

H-Index - 98

eISSN - 1530-9312

pISSN - 0891-2017

DOI - 10.1162/coli.2006.32.1.13

Subject(s) - wordnet , computer science , semantic similarity , natural language processing , spelling , artificial intelligence , lexical database , proxy (statistics) , similarity (geometry) , information retrieval , measure (data warehouse) , linguistics , machine learning , data mining , philosophy , image (mathematics)

The quantification of lexical semantic relatedness has many applications in NLP, and many different measures have been proposed. We evaluate five of these measures, all of which use WordNet as their central resource, by comparing their performance in detecting and correcting real-word spelling errors. An information-content-based measure proposed by Jiang and Conrath is found superior to those proposed by Hirst and St-Onge, Leacock and Chodorow, Lin, and Resnik. In addition, we explain why distributional similarity is not an adequate proxy for lexical semantic relatedness.

The content you want is available to Zendy users.

Already have an account? Click here to sign in.

Having issues? You can contact us here

Accelerating Research