2021
DOI: 10.1145/3440755
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Evolution of Semantic Similarity—A Survey

Abstract: Estimating the semantic similarity between text data is one of the challenging and open research problems in the field of Natural Language Processing (NLP). The versatility of natural language makes it difficult to define rule-based methods for determining semantic similarity measures. 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… Show more

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Cited by 207 publications
(135 citation statements)
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“…In the literature, there is a significant amount of works addressing semantic similarity [11], [12]. With the advent of Wikipedia, the most widely used and up-to-date knowledge repository, several approaches have been proposed by exploiting its features, such as articles, hyperlinks, categories, etc.. (see for instance [22], [25], [27], [31]).…”
Section: Related Workmentioning
confidence: 99%
See 1 more Smart Citation
“…In the literature, there is a significant amount of works addressing semantic similarity [11], [12]. With the advent of Wikipedia, the most widely used and up-to-date knowledge repository, several approaches have been proposed by exploiting its features, such as articles, hyperlinks, categories, etc.. (see for instance [22], [25], [27], [31]).…”
Section: Related Workmentioning
confidence: 99%
“…The information-theoretic definitions of semantic similarity defined by Resnik in [37], [38] and by Lin in [32], more than two decades ago, have been extensively mentioned and investigated in the literature, and a significant amount of similarity measures have been proposed originating from them, by relying on the information content approach [11], [12]. It is based on a probabilistic model that can be applied not only to concepts organized according to an ISA taxonomy (taxonomy for short), but also to ordinal values, feature vectors, and words.…”
Section: Introductionmentioning
confidence: 99%
“…Chandrasekaran et al determines the evolution of several available semantic similarity methods and reviews their pros and cons. Classifies by the underlying policies as corpusbased, hybrid approaches, knowledge-based, and deep neural network-based methods [22].…”
Section: Review Of the State Of The Artmentioning
confidence: 99%
“…Among the many language processing tasks with potential clinical utility that transformer-based models have excelled at is that of semantic classi cation [8]. The sentence embeddings in the form of xed-length vectors innately facilitate the text comparison process, and similarities between texts can be trivially obtained by applying distance metrics directly to the embeddings [9]. However, mere vector similarity between the text embeddings has limited applicability in the clinical context.…”
Section: Introductionmentioning
confidence: 99%