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| Оценяване на кохерентността на текст× | BERT Embeddings× | |
|---|---|---|
| Област | Извличане на текст | Извличане на текст |
| Семейство | Process / pipeline | Process / pipeline |
| Година на възникване≠ | 2008 | 2019 |
| Създател≠ | Barzilay & Lapata | Devlin, Chang, Lee & Toutanova (Google AI) |
| Тип≠ | NLP text-level scoring task | Contextual transformer text-representation method |
| Основополагащ източник≠ | Barzilay, R. & Lapata, M. (2008). Modeling Local Coherence: An Entity-Based Approach. Computational Linguistics, 34(1), 1-34. DOI ↗ | 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 ↗ |
| Други названия | coherence modeling, local coherence assessment, Metin Tutarlılık Puanlaması | contextual embeddings, transformer embeddings, BERT Tabanlı Metin Gömülmeleri |
| Свързани | 4 | 4 |
| Резюме≠ | 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. |
| ScholarGateНабор от данни ↗ |
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