Methoden vergelijken

	Named Entity Recognition (NER)	Informatieverwerving	Relatie-extractie	Tekstclassificatie
Vakgebied	Tekstmining	Tekstmining	Tekstmining	Tekstmining
Familie	Process / pipeline	Process / pipeline	Process / pipeline	Process / pipeline
Jaar van ontstaan	—	—	—	—
Grondlegger	—	—	—	—
Type	NLP sequence-labelling task	NLP structured-information task	NLP information-extraction task	Supervised NLP classification task
Oorspronkelijke bron	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.	Joachims, T. (1998). Text Categorization with Support Vector Machines: Learning with Many Relevant Features. ECML 1998. Lecture Notes in Computer Science, vol 1398. Springer.
Aliassen	NER, entity tagging, Adlandırılmış Varlık Tanıma (NER)	IE, structured information extraction, Bilgi Çıkarma (Information Extraction)	semantic relation extraction, İlişki Çıkarma (Relation Extraction)	text categorization, document classification, topic classification, metin sınıflandırma
Verwant	3	4	4	4
Samenvatting	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.	Text classification, also called text categorization, is a supervised natural-language-processing task that automatically assigns documents to predefined categories. Building on the support-vector-machine approach to text categorization established by Joachims (1998) and consolidated in the text-mining literature by Aggarwal and Zhai (2012), it powers tasks such as spam detection and topic classification by learning from labelled examples.