Võrdle meetodeid

	Entiteedi sidumine	Informatsiooni ekstraheerimine	Nimetatud üksuste äratundmine (NER)
Valdkond	Tekstikaeve	Tekstikaeve	Tekstikaeve
Perekond	Process / pipeline	Process / pipeline	Process / pipeline
Tekkeaasta	2008	—	—
Looja	Milne & Witten	—	—
Tüüp	NLP knowledge-base grounding task	NLP structured-information task	NLP sequence-labelling task
Algallikas	Milne, D. & Witten, I.H. (2008). Learning to Link with Wikipedia. CIKM (Proceedings of the 17th ACM Conference on Information and Knowledge Management).	Cowie, J. & Lehnert, W. (1996). Information Extraction. Communications of the ACM.	Nadeau, D. & Sekine, S. (2007). A survey of named entity recognition. Lingvisticae Investigationes.
Rööpnimetused	named entity disambiguation, entity disambiguation, entity resolution to knowledge base, Varlık Bağlama (Entity Linking)	IE, structured information extraction, Bilgi Çıkarma (Information Extraction)	NER, entity tagging, Adlandırılmış Varlık Tanıma (NER)
Seotud	3	4	3
Kokkuvõte	Entity linking is a natural-language-processing task that matches ambiguous entity mentions in text — people, places, organisations — to the correct record in a knowledge base such as Wikidata, DBpedia, or a domain dictionary. Surveyed and shaped by Milne and Witten (2008) and later neural approaches reviewed by Sevgili and colleagues (2022), it grounds free text into structured, unambiguous references used in knowledge-graph building and multi-source text analysis.	Information extraction (IE) is a natural-language-processing task that converts unstructured text into structured information — such as events, relations, and attributes — so that facts buried in free-form documents become machine-readable records. The task was consolidated in early surveys by Cowie and Lehnert (1996) and later by Grishman (2012).	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.