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| Pengekstrakan Ungkapan Temporal (TIMEX)× | Pengecaman Entiti Bernama (NER)× | |
|---|---|---|
| Bidang | Perlombongan Teks | Perlombongan Teks |
| Keluarga | Process / pipeline | Process / pipeline |
| Tahun asal | — | — |
| Pengasas | — | — |
| Jenis≠ | NLP information-extraction task | NLP sequence-labelling task |
| Sumber perintis≠ | 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 ↗ |
| Alias≠ | TIMEX, temporal tagging, TIMEX3 extraction, Zamansal İfade Çıkarma (TIMEX) | NER, entity tagging, Adlandırılmış Varlık Tanıma (NER) |
| Berkaitan≠ | 2 | 3 |
| Ringkasan≠ | 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. |
| ScholarGateSet data ↗ |
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