Difference between revisions of "Textual Entailment Portal"

From ACL Wiki
Jump to: navigation, search
(reference)
Line 3: Line 3:
 
An example of a positive TE (text entails hypothesis) is:
 
An example of a positive TE (text entails hypothesis) is:
 
* text: ''If you help the needy, God will reward you''.
 
* text: ''If you help the needy, God will reward you''.
* hypothesis: ''Giving money to a poor man has good consequences''.
+
* hypothesis: ''Giving money to [http://www.thai-sbobet.com sbobet]a poor man has good consequences''.
  
 
An example of a negative TE (text contradicts hypothesis) is:
 
An example of a negative TE (text contradicts hypothesis) is:

Revision as of 04:13, 25 June 2012

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 sbobeta 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/>