手法を比較

	Text Coherence Scoring	BERT埋め込み
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	2008	2019
提唱者	Barzilay & Lapata	Devlin, Chang, Lee & Toutanova (Google AI)
種類	NLP text-level scoring task	Contextual transformer text-representation method
原典	Barzilay, R. & Lapata, M. (2008). Modeling Local Coherence: An Entity-Based Approach. Computational Linguistics, 34(1), 1-34.	Devlin, J., Chang, M.-W., Lee, K. & Toutanova, K. (2019). BERT: Pre-training of Deep Bidirectional Transformers for Language Understanding. NAACL-HLT, 4171-4186.
別名	coherence modeling, local coherence assessment, Metin Tutarlılık Puanlaması	contextual embeddings, transformer embeddings, BERT Tabanlı Metin Gömülmeleri
関連	4	4
概要	Text coherence scoring computes a document-level coherence score with machine learning, rooted in the entity-based local coherence model introduced by Barzilay and Lapata (2008). It measures how well the sentences of a text hang together, using either an entity-grid model, a graph-based approach, or a transformer-based model.	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.