Comparer des méthodes

	Résolution de coréférences	Réponse aux questions (QA)
Domaine	Fouille de textes	Fouille de textes
Famille	Process / pipeline	Process / pipeline
Année d'origine	1978	—
Auteur d'origine	Hobbs (1978); Lee et al. (2017, neural end-to-end)	—
Type	NLP information-extraction task	NLP text-comprehension task
Source fondatrice	Lee, K. et al. (2017). End-to-end Neural Coreference Resolution. EMNLP.	Rajpurkar, P. et al. (2016). SQuAD: 100,000+ Questions for Machine Comprehension of Text. EMNLP.
Alias	coreference, anaphora resolution, Eşgönderim Çözümleme (Coreference Resolution)	QA, machine reading comprehension, Soru Cevaplama (Question Answering)
Apparentées	4	4
Résumé	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.