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textual entailment
Textual entailment (TE) in natural language processing is a directional relation between text fragments. The relation holds whenever the truth of one text fragment follows from another text. In the TE framework, the entailing and entailed texts are termed ''text (t)'' and ''hypothesis (h)'', respectively. Textual entailment is not the same as pure logical entailment- it has a more relaxed definition: "''t'' entails ''h''" (''t'' ⇒ ''h'') if, typically, a human reading ''t'' would infer that ''h'' is most likely true.〔(Ido Dagan, Oren Glickman and Bernardo Magnini. The PASCAL Recognising Textual Entailment Challenge, p. 2 ) ''in:'' Quiñonero-Candela, J.; Dagan, I.; Magnini, B.; d'Alché-Buc, F. (Eds.) ''Machine Learning Challenges. Lecture Notes in Computer Science'' , Vol. 3944, pp. 177-190, Springer, 2006.〕 The relation is directional because even if "''t'' entails ''h''", the reverse "''h'' entails ''t''" is much less certain.〔(Dagan, I. and O. Glickman. 'Probabilistic textual entailment: Generic applied modeling of language variability' ) in: ''PASCAL Workshop on Learning Methods for Text Understanding and Mining'' (2004) Grenoble.〕〔(Tătar, D. e.a. ''Textual Entailment as a Directional Relation'' )〕
==Ambiguity of natural language==
A characteristic of natural language is that there are many different ways to state what you want to say: several meanings can be contained in a single text and that the same meaning can be expressed by different texts. This variability of semantic expression can be seen as the dual problem of language ambiguity. Together they result in a many-to-many mapping between language expressions and meanings. Interpreting a text correctly would, in theory, require a thorough semantic interpretation into a logic-based representation of its meanings. Practical solutions for natural language processing seek to go not that deep and use textual entailment in a more shallow way.
抄文引用元・出典: フリー百科事典『 ウィキペディア（Wikipedia）』
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