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| NLP를 위한 대조 학습× | 의미론적 유사성× | |
|---|---|---|
| 분야 | 텍스트 마이닝 | 텍스트 마이닝 |
| 계열 | Process / pipeline | Process / pipeline |
| 기원 연도≠ | 2020–2021 | 2019 |
| 창시자≠ | Gao, Yao & Chen (SimCSE, 2021); Khosla et al. (Supervised Contrastive, 2020) | Nils Reimers & Iryna Gurevych (Sentence-BERT) |
| 유형≠ | Self-supervised / supervised representation learning | NLP text-comparison task |
| 원전≠ | Gao, T., Yao, X., & Chen, D. (2021). SimCSE: Simple Contrastive Learning of Sentence Embeddings. Proceedings of EMNLP 2021. link ↗ | Reimers, N. & Gurevych, I. (2019). Sentence-BERT: Sentence Embeddings using Siamese BERT-Networks. EMNLP. link ↗ |
| 별칭≠ | SimCSE, contrastive sentence embeddings, ContrastiveBERT, Karşıtlık Öğrenmesi — NLP (Contrastive Learning) | semantic textual similarity, text similarity, Anlamsal Benzerlik Analizi |
| 관련 | 4 | 4 |
| 요약≠ | Contrastive learning for NLP is a representation-learning technique — popularised by SimCSE (Gao et al., 2021) and Supervised Contrastive Learning (Khosla et al., 2020) — that trains a text encoder by pulling embeddings of similar text pairs together while pushing embeddings of dissimilar pairs apart. The result is a dense, high-quality embedding space that can be learned with no labels at all, or with minimal supervision, making it especially valuable when annotated data are scarce. | 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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