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| Tværsproglig tekstanalyse× | BERT-indlejringer× | |
|---|---|---|
| Fagområde | Tekstmining | Tekstmining |
| Familie | Process / pipeline | Process / pipeline |
| Oprindelsesår≠ | — | 2019 |
| Ophavsperson≠ | — | Devlin, Chang, Lee & Toutanova (Google AI) |
| Type≠ | Multilingual NLP representation task | Contextual transformer text-representation method |
| Oprindelig kilde≠ | Conneau, A. et al. (2020). Unsupervised Cross-lingual Representation Learning at Scale. Proceedings of ACL. 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 ↗ |
| Aliasser | multilingual text analysis, cross-lingual representation learning, Çok Dilli Metin Analizi (Cross-lingual) | contextual embeddings, transformer embeddings, BERT Tabanlı Metin Gömülmeleri |
| Relaterede | 4 | 4 |
| Resumé≠ | Cross-lingual text analysis lets you compare and analyse texts written in different languages within a shared vector space. Building on multilingual representation learning surveyed by Conneau et al. (2020) and Pires et al. (2019), it maps documents from several languages into one common embedding space so multilingual corpora can be studied together. | 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. |
| ScholarGateDatasæt ↗ |
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