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| Daudzvalodu tekstu analīze× | Tekstu klasifikācija× | |
|---|---|---|
| Nozare | Teksta ieguve | Teksta ieguve |
| Saime | Process / pipeline | Process / pipeline |
| Izcelsmes gads | — | — |
| Autors | — | — |
| Tips≠ | Multilingual NLP representation task | Supervised NLP classification task |
| Pirmavots≠ | Conneau, A. et al. (2020). Unsupervised Cross-lingual Representation Learning at Scale. Proceedings of ACL. DOI ↗ | Joachims, T. (1998). Text Categorization with Support Vector Machines: Learning with Many Relevant Features. ECML 1998. Lecture Notes in Computer Science, vol 1398. Springer. DOI ↗ |
| Citi nosaukumi≠ | multilingual text analysis, cross-lingual representation learning, Çok Dilli Metin Analizi (Cross-lingual) | text categorization, document classification, topic classification, metin sınıflandırma |
| Saistītās | 4 | 4 |
| Kopsavilkums≠ | 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. | Text classification, also called text categorization, is a supervised natural-language-processing task that automatically assigns documents to predefined categories. Building on the support-vector-machine approach to text categorization established by Joachims (1998) and consolidated in the text-mining literature by Aggarwal and Zhai (2012), it powers tasks such as spam detection and topic classification by learning from labelled examples. |
| ScholarGateDatu kopa ↗ |
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