Process / pipeline

Semantic Similarity — Measuring Meaning Between Texts

Semantic similarity analysis measures how close in meaning two texts are, rather than how many words they share on the surface. Building on the Sentence-BERT work of Reimers and Gurevych (2019), it represents each text as a vector and compares those vectors so that paraphrases score high even when their wording differs.

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Sources

Reimers, N. & Gurevych, I. (2019). Sentence-BERT: Sentence Embeddings using Siamese BERT-Networks. EMNLP. link ↗
Agirre, E. et al. (2013). *SEM 2013 shared task: Semantic Textual Similarity. ACL (*SEM). link ↗

Referenced by

Contrastive Learning for NLP Information Extraction Relation Extraction Text Summarization

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