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| Крос-езиков текстов анализ× | Класификация на текст× | |
|---|---|---|
| Област | Извличане на текст | Извличане на текст |
| Семейство | Process / pipeline | Process / pipeline |
| Година на възникване | — | — |
| Създател | — | — |
| Тип≠ | Multilingual NLP representation task | Supervised NLP classification task |
| Основополагащ източник≠ | 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 ↗ |
| Други названия≠ | multilingual text analysis, cross-lingual representation learning, Çok Dilli Metin Analizi (Cross-lingual) | text categorization, document classification, topic classification, metin sınıflandırma |
| Свързани | 4 | 4 |
| Резюме≠ | 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. |
| ScholarGateНабор от данни ↗ |
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