S. Padó, und I. Dagan. Oxford Handbook of Computational Linguistics, Oxford University Press, 2nd Edition, (2016)
Zusammenfassung
Textual entailment is a binary relation between two natural-language texts (called ‘text’ and ‘hypothesis’), where readers of the ‘text’ would agree the ‘hypothesis’ is most likely true (Peter is snoring → A man sleeps). Its recognition requires an account of linguistic variability ( an event may be realized in different ways, e.g. Peter buys the car ↔ The car is purchased by Peter) and of relationships between events (e.g. Peter buys the car → Peter owns the car). Unlike logics-based inference, textual entailment also covers cases of probable but still defeasible entailment (A hurricane hit Peter’s town → Peter’s town was damaged). Since human common-sense reasoning often involves such defeasible inferences, textual entailment is of considerable interest for real-world language processing tasks, as a generic, application-independent framework for semantic inference. This chapter discusses the history of textual entailment, approaches to recognizing it, and its integration in various NLP tasks.
%0 Book Section
%1 pado10:_textual_entail
%A Padó, Sebastian
%A Dagan, Ido
%B Oxford Handbook of Computational Linguistics
%D 2016
%E Mitkov, Ruslav
%I Oxford University Press
%K chapter imported myown
%T Textual Entailment
%U http://dx.doi.org/10.1093/oxfordhb/9780199573691.013.024
%X Textual entailment is a binary relation between two natural-language texts (called ‘text’ and ‘hypothesis’), where readers of the ‘text’ would agree the ‘hypothesis’ is most likely true (Peter is snoring → A man sleeps). Its recognition requires an account of linguistic variability ( an event may be realized in different ways, e.g. Peter buys the car ↔ The car is purchased by Peter) and of relationships between events (e.g. Peter buys the car → Peter owns the car). Unlike logics-based inference, textual entailment also covers cases of probable but still defeasible entailment (A hurricane hit Peter’s town → Peter’s town was damaged). Since human common-sense reasoning often involves such defeasible inferences, textual entailment is of considerable interest for real-world language processing tasks, as a generic, application-independent framework for semantic inference. This chapter discusses the history of textual entailment, approaches to recognizing it, and its integration in various NLP tasks.
%7 2nd
@incollection{pado10:_textual_entail,
abstract = {Textual entailment is a binary relation between two natural-language texts (called ‘text’ and ‘hypothesis’), where readers of the ‘text’ would agree the ‘hypothesis’ is most likely true (Peter is snoring → A man sleeps). Its recognition requires an account of linguistic variability ( an event may be realized in different ways, e.g. Peter buys the car ↔ The car is purchased by Peter) and of relationships between events (e.g. Peter buys the car → Peter owns the car). Unlike logics-based inference, textual entailment also covers cases of probable but still defeasible entailment (A hurricane hit Peter’s town → Peter’s town was damaged). Since human common-sense reasoning often involves such defeasible inferences, textual entailment is of considerable interest for real-world language processing tasks, as a generic, application-independent framework for semantic inference. This chapter discusses the history of textual entailment, approaches to recognizing it, and its integration in various NLP tasks.},
added-at = {2017-04-03T19:28:00.000+0200},
author = {Pad\'o, Sebastian and Dagan, Ido},
biburl = {https://puma.ub.uni-stuttgart.de/bibtex/2d0c09051829812bc2625720343ce2292/sp},
booktitle = {Oxford Handbook of Computational Linguistics},
edition = {2nd},
editor = {Mitkov, Ruslav},
interhash = {e1dfa593130c64f85ed80203715888fc},
intrahash = {d0c09051829812bc2625720343ce2292},
keywords = {chapter imported myown},
publisher = {Oxford University Press},
timestamp = {2017-04-03T17:28:18.000+0200},
title = {Textual Entailment},
url = {http://dx.doi.org/10.1093/oxfordhb/9780199573691.013.024},
year = 2016
}