Towards optimize-ESA for text semantic similarity: A case study of biomedical text

TitreTowards optimize-ESA for text semantic similarity: A case study of biomedical text
Publication TypeJournal Article
Year of Publication2020
AuthorsMrhar, K, Abik, M
JournalInternational Journal of Electrical and Computer Engineering
Volume10
Pagination2934-2943
Abstract

Explicit Semantic Analysis (ESA) is an approach to measure the semantic relatedness between terms or documents based on similarities to documents of a references corpus usually Wikipedia. ESA usage has received tremendous attention in the field of natural language processing NLP and information retrieval. However, ESA utilizes a huge Wikipedia index matrix in its interpretation by multiplying a large matrix by a term vector to produce a high-dimensional vector. Consequently, the ESA process is too expensive in interpretation and similarity steps. Therefore, the efficiency of ESA will slow down because we lose a lot of time in unnecessary operations. This paper propose enhancements to ESA called optimize-ESA that reduce the dimension at the interpretation stage by computing the semantic similarity in a specific domain. The experimental results show clearly that our method correlates much better with human judgement than the full version ESA approach. Copyright © 2020 Institute of Advanced Engineering and Science. All rights reserved.

URLhttps://www.scopus.com/inward/record.uri?eid=2-s2.0-85079383531&doi=10.11591%2fijece.v10i3.pp2934-2943&partnerID=40&md5=2965a06637b8b80abe9288b66e1082e7
DOI10.11591/ijece.v10i3.pp2934-2943
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