Comparar métodos

	Reconhecimento de Entidades Nomeadas (NER)	Extração de Informação	Classificação de Texto
Área	Mineração de texto	Mineração de texto	Mineração de texto
Família	Process / pipeline	Process / pipeline	Process / pipeline
Ano de origem	—	—	—
Autor original	—	—	—
Tipo	NLP sequence-labelling task	NLP structured-information task	Supervised NLP classification task
Fonte seminal	Nadeau, D. & Sekine, S. (2007). A survey of named entity recognition. Lingvisticae Investigationes.	Cowie, J. & Lehnert, W. (1996). Information Extraction. Communications of the ACM.	Joachims, T. (1998). Text Categorization with Support Vector Machines: Learning with Many Relevant Features. ECML 1998. Lecture Notes in Computer Science, vol 1398. Springer.
Outros nomes	NER, entity tagging, Adlandırılmış Varlık Tanıma (NER)	IE, structured information extraction, Bilgi Çıkarma (Information Extraction)	text categorization, document classification, topic classification, metin sınıflandırma
Relacionados	3	4	4
Resumo	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).	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.