Compara mètodes

	Inferència textual	Classificació de text
Camp	Mineria de text	Mineria de text
Família	Process / pipeline	Process / pipeline
Any d'origen	—	—
Autor original	—	—
Tipus	NLP sentence-pair classification task	Supervised NLP classification task
Font seminal	Dagan, I., Glickman, O. & Magnini, B. (2006). The PASCAL Recognising Textual Entailment Challenge.	Joachims, T. (1998). Text Categorization with Support Vector Machines: Learning with Many Relevant Features. ECML 1998. Lecture Notes in Computer Science, vol 1398. Springer.
Àlies	natural language inference, NLI, recognising textual entailment, RTE	text categorization, document classification, topic classification, metin sınıflandırma
Relacionats	4	4
Resum	Textual entailment, also known as natural language inference (NLI), is the natural-language-processing task of deciding whether one piece of text (the premise) entails a second piece of text (the hypothesis), contradicts it, or is neutral with respect to it. Formalised by the PASCAL Recognising Textual Entailment Challenge (Dagan, Glickman & Magnini, 2006) and broadened by the MultiNLI corpus (Williams, Nangia & Bowman, 2018), it underpins question answering and fact-verification pipelines.	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.