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| Trích xuất thông tin× | Trích xuất quan hệ× | Phân loại văn bản× | |
|---|---|---|---|
| Lĩnh vực | Khai phá văn bản | Khai phá văn bản | Khai phá văn bản |
| Họ | Process / pipeline | Process / pipeline | Process / pipeline |
| Năm ra đời | — | — | — |
| Người khởi xướng | — | — | — |
| Loại≠ | NLP structured-information task | NLP information-extraction task | Supervised NLP classification task |
| Công trình gốc≠ | Cowie, J. & Lehnert, W. (1996). Information Extraction. Communications of the ACM. DOI ↗ | Zelenko, D., Aone, C. & Richardella, A. (2003). Kernel Methods for Relation Extraction. Journal of Machine Learning Research, 3, 1083-1106. 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 ↗ |
| Tên gọi khác≠ | IE, structured information extraction, Bilgi Çıkarma (Information Extraction) | semantic relation extraction, İlişki Çıkarma (Relation Extraction) | text categorization, document classification, topic classification, metin sınıflandırma |
| Liên quan | 4 | 4 | 4 |
| Tóm tắt≠ | Information extraction (IE) is a natural-language-processing task that converts unstructured text into structured information — such as events, relations, and attributes — so that facts buried in free-form documents become machine-readable records. The task was consolidated in early surveys by Cowie and Lehnert (1996) and later by Grishman (2012). | Relation extraction is a natural-language-processing task that detects and classifies the semantic relations that hold between entities mentioned in text. Building on early kernel-based methods (Zelenko and colleagues, 2003) and later neural matching approaches (Baldini Soares and colleagues, 2019), it turns free-form text into structured facts of the form entity–relation–entity. | 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. |
| ScholarGateBộ dữ liệu ↗ |
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