方法对比
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| 共指消解× | 问答 (QA)× | |
|---|---|---|
| 领域 | 文本挖掘 | 文本挖掘 |
| 方法族 | Process / pipeline | Process / pipeline |
| 起源年份≠ | 1978 | — |
| 提出者≠ | Hobbs (1978); Lee et al. (2017, neural end-to-end) | — |
| 类型≠ | NLP information-extraction task | NLP text-comprehension task |
| 开创性文献≠ | Lee, K. et al. (2017). End-to-end Neural Coreference Resolution. EMNLP. link ↗ | Rajpurkar, P. et al. (2016). SQuAD: 100,000+ Questions for Machine Comprehension of Text. EMNLP. DOI ↗ |
| 别名 | coreference, anaphora resolution, Eşgönderim Çözümleme (Coreference Resolution) | QA, machine reading comprehension, Soru Cevaplama (Question Answering) |
| 相关 | 4 | 4 |
| 摘要≠ | Coreference resolution is a natural-language-processing task that detects when different expressions in a text refer to the same entity — for example a name, a later pronoun, and a descriptive phrase all pointing at one person. Rooted in early linguistic work by Hobbs (1978) and advanced by the end-to-end neural model of Lee et al. (2017), it improves the quality of information extraction and text understanding. | 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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