A New Approach for Calculating Semantic Similarity between Words Using WordNet and Set Theory | Zendy

Hanane Ezzikouri | Zendy; Youness Madani | Zendy; Mohammed Erritali | Zendy; Mohamed Oukessou | Zendy

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A New Approach for Calculating Semantic Similarity between Words Using WordNet and Set Theory

Author(s) -

Hanane Ezzikouri,

Youness Madani,

Mohammed Erritali,

Mohamed Oukessou

Publication year - 2019

Publication title -

procedia computer science

Language(s) - English

Resource type - Journals

SCImago Journal Rank - 0.334

H-Index - 76

ISSN - 1877-0509

DOI - 10.1016/j.procs.2019.04.182

Subject(s) - wordnet , computer science , natural language processing , semantic similarity , set (abstract data type) , task (project management) , artificial intelligence , similarity (geometry) , word (group theory) , lexical database , information retrieval , image (mathematics) , linguistics , philosophy , management , economics , programming language

Calculating semantic similarity between words is a challenging task of a lot of domains such as Natural language processing (NLP), information retrieval and plagiarism detection. WordNet is a lexical dictionary conceptually organized, where each concept has several characteristics: Synsets and Glosses. Synset represent sets of synonyms of a given word and Glosses are a short description. In this paper, we propose a new approach for calculating semantic similarity between two concepts. The proposed method is based on set theory’s concepts and WordNet properties, by calculating the relatedness between the synsets’ and glosses’s of the two concepts.

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