The DOI matches its Crossref record; 79 of the 79 references it cites were found in a registry or an index.
Evolution of Semantic Similarity—A Survey
Open accessAbstract
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 most recent research work on transformer-based models, categorizing them based on their underlying principles as knowledge-based, corpus-based, deep neural network–based 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.
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“The Lesk measure[10], assigns a value of relatedness between two words based on the overlap of words in their gloss and the glosses of the concepts they are related to in an ontology like WordNet[43].”
MethodInfluentialDeep Contextualized Word Representations
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Enriching Word Vectors with Subword Information
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SciBERT: A Pretrained Language Model for Scientific Text
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Cited by
Distributional Semantics: Meaning Through Culture and Interaction
Contreras Kallens, P., & Christiansen, M. H. (2025) · Topics in Cognitive Science · doi:10.1111/tops.12771 · Cited by 17
The Geometry of Concepts: Sparse Autoencoder Feature Structure
Li, Y., Michaud, E. J., Baek, D. D., Engels, J., Sun, X., & Tegmark, M. (2024) · arXiv · doi:10.48550/arxiv.2410.19750 · Cited by 14
LEGO-GraphRAG: Modularizing Graph-Based Retrieval-Augmented Generation for Design Space Exploration
Cao, Y., Gao, Z., Li, Z., Xie, X., Zhou, S. K., & Xu, J. (2025) · Proceedings of the VLDB Endowment · doi:10.14778/3748191.3748194 · Cited by 11
Gilani, K., Verket, M., Peters, C., Dumontier, M., Rocca, H.-P. B.-L., & Urovi, V. (2025) · Computers in Biology and Medicine · doi:10.1016/j.compbiomed.2025.110745 · Cited by 5
Metadata
- Authors
- Chandrasekaran, D., & Mago, V.
- Venue
- ACM Computing Surveys
- Published
- 2021-02-18
- Volume
- 54
- Issue
- 2
- Pages
- 1-37
- ISSN
- 0360-0300 (ISSN-L)
- 1557-7341
- OpenAlex
- W3021792233
- Cited by
- 303
- References
- 79
- FWCI
- 15.79
- Type
- journal article
- Language
- en
- Open access
- green
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