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To address this issue, various semantic similarity methods have been proposed over the years. This survey article traces the evolution of such methods beginning from traditional NLP techniques such as kernel-based methods to the most recent research work on transformer-based models, categorizing them based on their underlying principles as knowledge-based, corpus-based, deep neural network\u2013based methods, and hybrid methods. Discussing the strengths and weaknesses of each method, this survey provides a comprehensive view of existing systems in place for new researchers to experiment and develop innovative ideas to address the issue of semantic similarity.<\/jats:p>","DOI":"10.1145\/3440755","type":"journal-article","created":{"date-parts":[[2021,2,21]],"date-time":"2021-02-21T00:36:10Z","timestamp":1613867770000},"page":"1-37","update-policy":"https:\/\/doi.org\/10.1145\/crossmark-policy","source":"Crossref","is-referenced-by-count":263,"title":["Evolution of Semantic Similarity\u2014A Survey"],"prefix":"10.1145","volume":"54","author":[{"given":"Dhivya","family":"Chandrasekaran","sequence":"first","affiliation":[{"name":"Lakehead University, Thunderbay, Ontario"}]},{"given":"Vijay","family":"Mago","sequence":"additional","affiliation":[{"name":"Lakehead University, Thunderbay, Ontario"}]}],"member":"320","published-online":{"date-parts":[[2021,2,18]]},"reference":[{"key":"e_1_2_1_1_1","doi-asserted-by":"publisher","DOI":"10.3115\/1620754.1620758"},{"key":"e_1_2_1_2_1","doi-asserted-by":"publisher","DOI":"10.18653\/v1\/S15-2045"},{"key":"e_1_2_1_3_1","doi-asserted-by":"publisher","DOI":"10.3115\/v1\/S14-2010"},{"key":"e_1_2_1_4_1","doi-asserted-by":"publisher","DOI":"10.18653\/v1\/S16-1081"},{"key":"e_1_2_1_5_1","unstructured":"Eneko Agirre Daniel Cer Mona Diab and Aitor Gonzalez-Agirre. 2012. 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