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| 타임라인 추출× | 개체 간의 의미 관계× | |
|---|---|---|
| 분야 | 텍스트 마이닝 | 텍스트 마이닝 |
| 계열 | Process / pipeline | Process / pipeline |
| 기원 연도≠ | 2010 (TempEval-2 benchmark) | — |
| 창시자≠ | TempEval shared task community (Verhagen et al., 2010) | — |
| 유형≠ | NLP structured information extraction task | NLP information-extraction task |
| 원전≠ | Verhagen, M. et al. (2010). SemEval-2010 Task 13: TempEval-2. Proceedings of the 5th International Workshop on Semantic Evaluation (ACL). link ↗ | Zelenko, D., Aone, C. & Richardella, A. (2003). Kernel Methods for Relation Extraction. Journal of Machine Learning Research, 3, 1083-1106. link ↗ |
| 별칭≠ | temporal event ordering, event timeline construction, Zaman Çizelgesi Çıkarma (Timeline Extraction) | semantic relation extraction, İlişki Çıkarma (Relation Extraction) |
| 관련 | 4 | 4 |
| 요약≠ | 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. | 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. |
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