Methoden vergleichen

	Benannte Entitätenerkennung (NER)	Information Extraction	Relationsextraktion
Fachgebiet	Text Mining	Text Mining	Text Mining
Familie	Process / pipeline	Process / pipeline	Process / pipeline
Entstehungsjahr	—	—	—
Urheber	—	—	—
Typ	NLP sequence-labelling task	NLP structured-information task	NLP information-extraction task
Wegweisende Quelle	Nadeau, D. & Sekine, S. (2007). A survey of named entity recognition. Lingvisticae Investigationes.	Cowie, J. & Lehnert, W. (1996). Information Extraction. Communications of the ACM.	Zelenko, D., Aone, C. & Richardella, A. (2003). Kernel Methods for Relation Extraction. Journal of Machine Learning Research, 3, 1083-1106.
Aliasnamen	NER, entity tagging, Adlandırılmış Varlık Tanıma (NER)	IE, structured information extraction, Bilgi Çıkarma (Information Extraction)	semantic relation extraction, İlişki Çıkarma (Relation Extraction)
Verwandt	3	4	4
Zusammenfassung	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.	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).	Relation extraction is a natural-language-processing task that detects and classifies the semantic relations that hold between entities mentioned in text. Building on early kernel-based methods (Zelenko and colleagues, 2003) and later neural matching approaches (Baldini Soares and colleagues, 2019), it turns free-form text into structured facts of the form entity–relation–entity.