Process / pipeline
BERT 嵌入 — 上下文文本表示
BERT 嵌入，由 Google AI 的 Devlin 及其同事于 2019 年推出，使用双向 Transformer 编码器将文本转换为上下文敏感的密集向量。由于词语的含义会随其上下文而变化，BERT 生成的表示比 Word2Vec 等静态方法或 LDA 等主题模型更为丰富。
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来源
- Devlin, J., Chang, M.-W., Lee, K. & Toutanova, K. (2019). BERT: Pre-training of Deep Bidirectional Transformers for Language Understanding. NAACL-HLT, 4171-4186. DOI: 10.18653/v1/N19-1423 ↗
- Tenney, I., Das, D. & Pavlick, E. (2019). BERT Rediscovers the Classical NLP Pipeline. Proceedings of the 57th Annual Meeting of the Association for Computational Linguistics (ACL), 4593-4601. DOI: 10.18653/v1/P19-1452 ↗
如何引用本页
ScholarGate. (2026, June 1). BERT-Based Text Embeddings. ScholarGate. https://scholargate.app/zh/text-mining/bert-embeddings
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