Difference between revisions of "Textual Entailment Portal"

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'''Textual Entailment''' (TE) is the task of judging whether the truth of one text fragment follows from another text. In the TE framework, the entailing and entailed texts are termed ''text'' and ''hypothesis'', respectively.  
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'''Textual Entailment''' (TE) 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'' and ''hypothesis'', respectively.  
  
 
An example of a positive TE (text entails hypothesis) is:
 
An example of a positive TE (text entails hypothesis) is:
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* hypothesis: ''Giving money to a poor man will make you better person''.
 
* hypothesis: ''Giving money to a poor man will make you better person''.
  
The entailment need not be pure logical - 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."<ref>I. Dagan</ref>
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The entailment need not be pure logical - 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."<ref>[http://u.cs.biu.ac.il/~dagan/publications/RTEChallenge.pdf 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.</ref>
  
 
Recognizing Textual Entailment (RTE) has been proposed recently as a generic task that captures major semantic inference needs across many natural language processing applications.
 
Recognizing Textual Entailment (RTE) has been proposed recently as a generic task that captures major semantic inference needs across many natural language processing applications.
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This page serves as a community portal for everything related to Textual Entailment:
 
This page serves as a community portal for everything related to Textual Entailment:
* [[Textual Entailment Resource Pool]] - Entailment engines, demos, knowledge resources, etc.
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* [[Textual Entailment Resource Pool]] - Complete RTE Systems, RTE data sets, Knowledge Resources, Tools (Parsers, Role Labelling, Entity Recognition Tools, Similarity / Relatedness Tools, Corpus Readers, Related Libraries), Links.
 
* PASCAL Challenge - [[Recognizing Textual Entailment|Recognizing Textual Entailment (RTE)]]
 
* PASCAL Challenge - [[Recognizing Textual Entailment|Recognizing Textual Entailment (RTE)]]
* [[Textual Entailment References]] - workshops, tutorials and papers.
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* [[Textual Entailment References]] - Workshops, Tutorials and Papers.
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==Notes==
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<references/>
  
 
[[Category:Textual Entailment Portal]]
 
[[Category:Textual Entailment Portal]]

Latest revision as of 03:42, 7 August 2013

Textual Entailment (TE) 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 and hypothesis, respectively.

An example of a positive TE (text entails hypothesis) is:

  • text: If you help the needy, God will reward you.
  • hypothesis: Giving money to a poor man has good consequences.

An example of a negative TE (text contradicts hypothesis) is:

  • text: If you help the needy, God will reward you.
  • hypothesis: Giving money to a poor man has no consequences.

An example of a non-TE (text does not entail nor contradict) is:

  • text: If you help the needy, God will reward you.
  • hypothesis: Giving money to a poor man will make you better person.

The entailment need not be pure logical - 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."<ref>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.</ref>

Recognizing Textual Entailment (RTE) has been proposed recently as a generic task that captures major semantic inference needs across many natural language processing applications.


This page serves as a community portal for everything related to Textual Entailment:

Notes

<references/>