Bandingkan metode

	Entity Linking	Deteksi Niat	Pengenalan Entitas Bernama (NER)
Bidang	Penambangan Teks	Penambangan Teks	Penambangan Teks
Keluarga	Process / pipeline	Process / pipeline	Process / pipeline
Tahun asal	2008	—	—
Pencetus	Milne & Witten	—	—
Tipe	NLP knowledge-base grounding task	NLP / NLU text-classification task	NLP sequence-labelling task
Sumber perintis	Milne, D. & Witten, I.H. (2008). Learning to Link with Wikipedia. CIKM (Proceedings of the 17th ACM Conference on Information and Knowledge Management).	Larson, S. et al. (2019). An Evaluation Dataset for Intent Classification and Out-of-Scope Prediction. EMNLP.	Nadeau, D. & Sekine, S. (2007). A survey of named entity recognition. Lingvisticae Investigationes.
Alias	named entity disambiguation, entity disambiguation, entity resolution to knowledge base, Varlık Bağlama (Entity Linking)	intent classification, intent recognition, Niyet Tespiti (Intent Detection)	NER, entity tagging, Adlandırılmış Varlık Tanıma (NER)
Terkait	3	4	3
Ringkasan	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.	Intent detection is a natural-language-understanding task that classifies the purpose behind a user utterance — such as making a reservation, asking for information, or filing a complaint — into one of a set of predefined intent classes. It is a core NLU component of conversational interfaces and customer-service automation systems, drawing on the benchmarks of Larson et al. (2019) and Casanueva et al. (2020).	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.