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

被引：16

作者：

Ezzikouri, Hanane ^{[1
]}

Madani, Youness ^{[1
]}

Erritali, Mohammed ^{[1
]}

Oukessou, Mohamed ^{[1
]}

机构：

[1] Sultan Moulay Slimane Univ, Fac Sci & Tech, Beni Mellal, Morocco

来源：

10TH INTERNATIONAL CONFERENCE ON AMBIENT SYSTEMS, NETWORKS AND TECHNOLOGIES (ANT 2019) / THE 2ND INTERNATIONAL CONFERENCE ON EMERGING DATA AND INDUSTRY 4.0 (EDI40 2019) / AFFILIATED WORKSHOPS | 2019年 / 151卷

关键词：

Semantic Similarity; Natural Language Processing; WordNet; Set Theory;

D O I：

10.1016/j.procs.2019.04.182

中图分类号：

TP39 [计算机的应用];

学科分类号：

081203 ; 0835 ;

摘要：

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. (C) 2019 The Authors. Published by Elsevier B.V. This is an open access article under the CC BY-NC-ND license (http://creativecommons.org/licenses/by-nc-nd/4.0/) Peer-review under responsibility of the Conference Program Chairs.

引用

页码：1261 / 1265

页数：5

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