পদ্ধতির তুলনা করুন

	আন্তঃনথি সত্তা ট্র্যাকিং	নামযুক্ত সত্তা শনাক্তকরণ (NER)
ক্ষেত্র	টেক্সট খনন	টেক্সট খনন
পরিবার	Process / pipeline	Process / pipeline
উদ্ভবের বছর	1998 (scoring foundations); 2019 (neural joint model)	—
প্রবর্তক	—	—
ধরন	NLP pipeline — cross-document coreference resolution	NLP sequence-labelling task
মৌলিক উৎস	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.
অপর নাম	cross-document coreference resolution, cross-doc entity linking, Belge Ötesi Varlık Takibi	NER, entity tagging, Adlandırılmış Varlık Tanıma (NER)
সম্পর্কিত	4	3
সারসংক্ষেপ	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.