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Revisa els mètodes seleccionats l'un al costat de l'altre; les files que difereixen es ressalten.
| Extracció d'expressions temporals (TIMEX)× | Reconeixement d'Entitats Nomenades (NER)× | Extracció de relacions× | |
|---|---|---|---|
| Camp | Mineria de text | Mineria de text | Mineria de text |
| Família | Process / pipeline | Process / pipeline | Process / pipeline |
| Any d'origen | — | — | — |
| Autor original | — | — | — |
| Tipus≠ | NLP information-extraction task | NLP sequence-labelling task | NLP information-extraction task |
| Font seminal≠ | Verhagen, M. et al. (2007). SemEval-2007 Task 15: TempEval Temporal Relation Identification. link ↗ | Nadeau, D. & Sekine, S. (2007). A survey of named entity recognition. Lingvisticae Investigationes. link ↗ | Zelenko, D., Aone, C. & Richardella, A. (2003). Kernel Methods for Relation Extraction. Journal of Machine Learning Research, 3, 1083-1106. link ↗ |
| Àlies≠ | TIMEX, temporal tagging, TIMEX3 extraction, Zamansal İfade Çıkarma (TIMEX) | NER, entity tagging, Adlandırılmış Varlık Tanıma (NER) | semantic relation extraction, İlişki Çıkarma (Relation Extraction) |
| Relacionats≠ | 2 | 3 | 4 |
| Resum≠ | Temporal expression extraction is a natural-language-processing task that detects dates, times, durations, and frequencies in text and normalises them to the TimeML/TIMEX3 standard. Building on the TempEval shared task introduced by Verhagen et al. (2007), it turns time references scattered through free text into structured, machine-readable values that support event timelines and chronological analysis. | Named entity recognition (NER) is a natural-language-processing task that automatically detects and labels entities in text — such as people, organisations, locations, and dates. Surveyed by Nadeau and Sekine (2007) and later advanced with neural architectures by Lample et al. (2016), it turns free-running text into tagged spans that downstream tools can use. | 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. |
| ScholarGateConjunt de dades ↗ |
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