השוואת שיטות

	היסק טקסטואלי	סיווג טקסט
תחום	כריית טקסט	כריית טקסט
משפחה	Process / pipeline	Process / pipeline
שנת המקור	—	—
הוגה השיטה	—	—
סוג	NLP sentence-pair classification task	Supervised NLP classification task
מקור מכונן	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.
כינויים	natural language inference, NLI, recognising textual entailment, RTE	text categorization, document classification, topic classification, metin sınıflandırma
קשורות	4	4
תקציר	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.