# Measuring Sentences Similarity: A Survey

arXiv: https://arxiv.org/abs/1910.03940 · HTML: https://mlsanity.com/paper/1910.03940/

## Key facts

- Cited by 9 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 6 Oct 2019.

## Built on by (most cited first, 9 of 9)

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- [AED: An black-box NLP classifier model attacker](https://mlsanity.com/paper/2112.11660/) (2021)
- [Suggesting Relevant Questions for a Query Using Statistical Natural Language Processing Technique](https://mlsanity.com/paper/2204.12069/) (2022)
- [SimClone: Detecting Tabular Data Clones using Value Similarity](https://mlsanity.com/paper/2407.12802/) (2024)
- [GRAD: Generative Retrieval-Aligned Demonstration Sampler for Efficient Few-Shot Reasoning](https://mlsanity.com/paper/2510.01165/) (2025)

## Abstract (arXiv)

This study is to review the approaches used for measuring sentences similarity. Measuring similarity between natural language sentences is a crucial task for many Natural Language Processing applications such as text classification, information retrieval, question answering, and plagiarism detection. This survey classifies approaches of calculating sentences similarity based on the adopted methodology into three categories. Word-to-word based, structure based, and vector-based are the most widely used approaches to find sentences similarity. Each approach measures relatedness between short texts based on a specific perspective. In addition, datasets that are mostly used as benchmarks for evaluating techniques in this field are introduced to provide a complete view on this issue. The approaches that combine more than one perspective give better results. Moreover, structure based similarity that measures similarity between sentences structures needs more investigation.