Salīdzināt metodes

	Entitāšu izsekošana starp dokumentiem	Nosaukuma entītiju atpazīšana (NER)
Nozare	Teksta ieguve	Teksta ieguve
Saime	Process / pipeline	Process / pipeline
Izcelsmes gads	1998 (scoring foundations); 2019 (neural joint model)	—
Autors	—	—
Tips	NLP pipeline — cross-document coreference resolution	NLP sequence-labelling task
Pirmavots	Bagga, A. & Baldwin, B. (1998). Algorithms for Scoring Coreference Chains. In Proceedings of the LREC 1998 Linguistic Coreference Workshop, pp. 563–566.	Nadeau, D. & Sekine, S. (2007). A survey of named entity recognition. Lingvisticae Investigationes.
Citi nosaukumi	cross-document coreference resolution, cross-doc entity linking, Belge Ötesi Varlık Takibi	NER, entity tagging, Adlandırılmış Varlık Tanıma (NER)
Saistītās	4	3
Kopsavilkums	Cross-document entity tracking, formally known as cross-document coreference resolution, identifies and merges all references to the same real-world entity scattered across a collection of documents. Rooted in the B3 evaluation framework introduced by Bagga and Baldwin (1998) and substantially advanced by the neural joint model of Barhom et al. (2019), the method builds entity clusters that span document boundaries — enabling multi-document understanding, knowledge-base population, and corpus-wide entity analysis.	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.