方法对比
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| 事件检测× | 命名实体识别 (NER)× | 问答 (QA)× | |
|---|---|---|---|
| 领域 | 文本挖掘 | 文本挖掘 | 文本挖掘 |
| 方法族 | Process / pipeline | Process / pipeline | Process / pipeline |
| 起源年份 | — | — | — |
| 提出者 | — | — | — |
| 类型≠ | NLP information-extraction task | NLP sequence-labelling task | NLP text-comprehension task |
| 开创性文献≠ | Doddington, G. et al. (2004). The Automatic Content Extraction (ACE) Program — Tasks, Data, and Evaluation. LREC. link ↗ | Nadeau, D. & Sekine, S. (2007). A survey of named entity recognition. Lingvisticae Investigationes. link ↗ | Rajpurkar, P. et al. (2016). SQuAD: 100,000+ Questions for Machine Comprehension of Text. EMNLP. DOI ↗ |
| 别名≠ | event extraction, Olay Tespiti (Event Detection) | NER, entity tagging, Adlandırılmış Varlık Tanıma (NER) | QA, machine reading comprehension, Soru Cevaplama (Question Answering) |
| 相关≠ | 4 | 3 | 4 |
| 摘要≠ | 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. | 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. | Question answering is a natural-language-processing task that automatically answers natural-language questions grounded in a given context passage, using either extractive or generative approaches. The task was crystallised by the SQuAD benchmark of Rajpurkar et al. (2016), and later models such as XLNet (Yang et al., 2019) pushed reading-comprehension accuracy higher. |
| ScholarGate数据集 ↗ |
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