Evolution of Semantic Similarity -- A Survey
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Abstract (arXiv)
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. In order to address this issue, various semantic similarity methods have been proposed over the years. This survey article traces the evolution of such methods, 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.
Key facts. Cited by 83 papers in the map (data through 14 Oct 2026). First posted on arXiv 19 Apr 2020.
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Citing papers per year: 2021: 6; 2022: 8; 2023: 22; 2024: 19; 2025: 19; 2026: 9.
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- 2024MoRSE: Bridging the Gap in Cybersecurity Expertise with Retrieval Augmented Generation22 Jul 2024
- 2021D2A: A Dataset Built for AI-Based Vulnerability Detection Methods Using Differential Analysiscited by 49
- 2022A Simple Approach to Improve Single-Model Deep Uncertainty via Distance-Awarenesscited by 49
- 2021Reinforcement Learning-powered Semantic Communication via Semantic Similaritycited by 15
- 2021What Makes Sentences Semantically Related: A Textual Relatedness Dataset and Empirical Studycited by 15
- 2022AEON: A Method for Automatic Evaluation of NLP Test Casescited by 15
- 2021conSultantBERT: Fine-tuned Siamese Sentence-BERT for Matching Jobs and Job Seekerscited by 13
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- 2026Characterising AI Models for Cataloguing13 Jul 2026
- 2026Prompting Complexity: Shortest Prompts for Texts and Behaviors in LLMs7 Jul 2026
- 2026Keyphrase Generative Representation of Youth Crisis Conversations Beyond Static Taxonomies26 May 2026
- 2026Benchmarking LLM-Based Static Analysis for Secure Smart Contract Development: Reliability, Limitations, and Potential Hybrid Solutions11 May 2026
- 2026IntentVLM: Open-Vocabulary Intention Recognition through Forward-Inverse Modeling with Video-Language Models27 Apr 2026
- 2026TIDE: Tuning-Integrated Dynamic Evolution for LLM-Based Automated Heuristic Design29 Jan 2026
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