Compară metode
Examinează metodele selectate una lângă alta; rândurile care diferă sunt evidențiate.
| Detecția Evenimentelor× | Clasificarea textului× | |
|---|---|---|
| Domeniu | Mineritul textelor | Mineritul textelor |
| Familie | Process / pipeline | Process / pipeline |
| Anul apariției | — | — |
| Autorul original | — | — |
| Tip≠ | NLP information-extraction task | Supervised NLP classification task |
| Sursa seminală≠ | Doddington, G. et al. (2004). The Automatic Content Extraction (ACE) Program — Tasks, Data, and Evaluation. LREC. 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 ↗ |
| Denumiri alternative≠ | event extraction, Olay Tespiti (Event Detection) | text categorization, document classification, topic classification, metin sınıflandırma |
| Înrudite | 4 | 4 |
| Rezumat≠ | Event detection is a natural-language-processing information-extraction task that finds events, historical developments, and action expressions in text and classifies them by type. It grew out of the Automatic Content Extraction (ACE) program described by Doddington et al. (2004) and is widely used in news analysis and historical research. | 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. |
| ScholarGateSet de date ↗ |
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