手法を比較

	BERT埋め込み	Doc2Vec
分野	テキストマイニング	テキストマイニング
系統	Process / pipeline	Process / pipeline
提唱年	2019	2014
提唱者	Devlin, Chang, Lee & Toutanova (Google AI)	Quoc V. Le & Tomas Mikolov
種類	Contextual transformer text-representation method	Document-embedding representation learning
原典	Devlin, J., Chang, M.-W., Lee, K. & Toutanova, K. (2019). BERT: Pre-training of Deep Bidirectional Transformers for Language Understanding. NAACL-HLT, 4171-4186.	Le, Q. V. & Mikolov, T. (2014). Distributed Representations of Sentences and Documents. Proceedings of the 31st International Conference on Machine Learning (ICML), 1188-1196.
別名	contextual embeddings, transformer embeddings, BERT Tabanlı Metin Gömülmeleri	paragraph vector, document embeddings, Doc2Vec Belge Gömülmeleri
関連	4	4
概要	BERT-based text embeddings, introduced by Devlin and colleagues at Google AI in 2019, turn text into context-sensitive dense vectors using a bidirectional Transformer encoder. Because the meaning of a word shifts with its context, BERT produces richer representations than static methods such as Word2Vec or topic models like LDA.	Doc2Vec, also known as Paragraph Vector, is a representation-learning method introduced by Le and Mikolov (2014) that maps whole documents to fixed-length dense vectors. These vectors place similar documents close together in space, supporting document comparison and classification.