Bandingkan metode

	Pelacakan Entitas Lintas Dokumen	Pengenalan Entitas Bernama (NER)
Bidang	Penambangan Teks	Penambangan Teks
Keluarga	Process / pipeline	Process / pipeline
Tahun asal	1998 (scoring foundations); 2019 (neural joint model)	—
Pencetus	—	—
Tipe	NLP pipeline — cross-document coreference resolution	NLP sequence-labelling task
Sumber perintis	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.
Alias	cross-document coreference resolution, cross-doc entity linking, Belge Ötesi Varlık Takibi	NER, entity tagging, Adlandırılmış Varlık Tanıma (NER)
Terkait	4	3
Ringkasan	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.