Paper Info
Title
A New Approach For Calculating Semantic Similarity Between Words Using Wordnet And Set Theory
Abstract
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.
Year
DOI
Venue
2019
10.1016/j.procs.2019.04.182
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
Keywords
DocType
Volume
Semantic Similarity, Natural Language Processing, WordNet, Set Theory
Conference
151
ISSN
Citations 
PageRank 
1877-0509
0
0.34
References 
Authors
0
4
Name
Order
Citations
PageRank
Hanane Ezzikouri111.72
Youness Madani244.13
Mohammed Erritali31410.03
Mohamed Oukessou400.68