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| Ekstrakcja osi czasu× | Klasyfikacja Tekstu× | |
|---|---|---|
| Dziedzina | Eksploracja tekstu | Eksploracja tekstu |
| Rodzina | Process / pipeline | Process / pipeline |
| Rok powstania≠ | 2010 (TempEval-2 benchmark) | — |
| Twórca≠ | TempEval shared task community (Verhagen et al., 2010) | — |
| Typ≠ | NLP structured information extraction task | Supervised NLP classification task |
| Źródło pierwotne≠ | Verhagen, M. et al. (2010). SemEval-2010 Task 13: TempEval-2. Proceedings of the 5th International Workshop on Semantic Evaluation (ACL). link ↗ | 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 ↗ |
| Inne nazwy≠ | temporal event ordering, event timeline construction, Zaman Çizelgesi Çıkarma (Timeline Extraction) | text categorization, document classification, topic classification, metin sınıflandırma |
| Pokrewne | 4 | 4 |
| Podsumowanie≠ | Timeline extraction is a natural-language-processing task that identifies events mentioned in text, anchors each event to a temporal expression, and arranges them into a chronologically ordered timeline. Formalised through the TempEval shared tasks (Verhagen et al., 2010), it enables automatic reconstruction of historical narratives, news event sequences, and clinical case progressions from unstructured text. | 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. |
| ScholarGateZbiór danych ↗ |
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