Salīdzināt metodes

	Koreferenču izšķiršana	Jautājumu atbildēšana (QA)
Nozare	Teksta ieguve	Teksta ieguve
Saime	Process / pipeline	Process / pipeline
Izcelsmes gads	1978	—
Autors	Hobbs (1978); Lee et al. (2017, neural end-to-end)	—
Tips	NLP information-extraction task	NLP text-comprehension task
Pirmavots	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.
Citi nosaukumi	coreference, anaphora resolution, Eşgönderim Çözümleme (Coreference Resolution)	QA, machine reading comprehension, Soru Cevaplama (Question Answering)
Saistītās	4	4
Kopsavilkums	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.