Linganisha mbinu

	Utatuzi wa Ulinganifu wa Marejeo	Utambuzi wa Majina ya Entiti (NER)
Nyanja	Uchimbaji wa Matini	Uchimbaji wa Matini
Familia	Process / pipeline	Process / pipeline
Mwaka wa asili	1978	—
Mwanzilishi	Hobbs (1978); Lee et al. (2017, neural end-to-end)	—
Aina	NLP information-extraction task	NLP sequence-labelling task
Chanzo asilia	Lee, K. et al. (2017). End-to-end Neural Coreference Resolution. EMNLP.	Nadeau, D. & Sekine, S. (2007). A survey of named entity recognition. Lingvisticae Investigationes.
Majina mbadala	coreference, anaphora resolution, Eşgönderim Çözümleme (Coreference Resolution)	NER, entity tagging, Adlandırılmış Varlık Tanıma (NER)
Zinazohusiana	4	3
Muhtasari	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.	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.