The goal of identifying textual entailment – whether one piece of text can be plausibly inferred from another – has emerged in recent years as a generic core problem in natural language
understanding. Work in this area has been largely driven by the PASCAL Recognizing Textual Entailment (RTE) challenges, which are a series of annual competitive meetings. The current work exhibits strong ties to some earlier lines of research, particularly automatic acquisition of paraphrases and lexical semantic relationships and unsupervised inference in
applications such as question answering, information extraction and summarization. It has also opened the way to newer lines of research on more involved inference methods, on
knowledge representations needed to support this natural language understanding challenge and on the use of learning methods in this context. RTE has fostered an active and growing community of researchers focused on the problem of applied entailment. This special issue of the JNLE provides an opportunity to showcase some of the most important work in this emerging area.
%0 Journal Article
%1 CambridgeJournals:6906264
%A Dagan, Ido
%A Dolan, Bill
%A Magnini, Bernardo
%A Roth, Dan
%D 2009
%J Natural Language Engineering
%K language natural textual_entailment
%N Special Issue 04
%P i-xvii
%R 10.1017/S1351324909990209
%T Recognizing textual entailment: Rational, evaluation and approaches
%U http://dx.doi.org/10.1017/S1351324909990209
%V 15
%X The goal of identifying textual entailment – whether one piece of text can be plausibly inferred from another – has emerged in recent years as a generic core problem in natural language
understanding. Work in this area has been largely driven by the PASCAL Recognizing Textual Entailment (RTE) challenges, which are a series of annual competitive meetings. The current work exhibits strong ties to some earlier lines of research, particularly automatic acquisition of paraphrases and lexical semantic relationships and unsupervised inference in
applications such as question answering, information extraction and summarization. It has also opened the way to newer lines of research on more involved inference methods, on
knowledge representations needed to support this natural language understanding challenge and on the use of learning methods in this context. RTE has fostered an active and growing community of researchers focused on the problem of applied entailment. This special issue of the JNLE provides an opportunity to showcase some of the most important work in this emerging area.
@article{CambridgeJournals:6906264,
abstract = {The goal of identifying textual entailment – whether one piece of text can be plausibly inferred from another – has emerged in recent years as a generic core problem in natural language
understanding. Work in this area has been largely driven by the PASCAL Recognizing Textual Entailment (RTE) challenges, which are a series of annual competitive meetings. The current work exhibits strong ties to some earlier lines of research, particularly automatic acquisition of paraphrases and lexical semantic relationships and unsupervised inference in
applications such as question answering, information extraction and summarization. It has also opened the way to newer lines of research on more involved inference methods, on
knowledge representations needed to support this natural language understanding challenge and on the use of learning methods in this context. RTE has fostered an active and growing community of researchers focused on the problem of applied entailment. This special issue of the JNLE provides an opportunity to showcase some of the most important work in this emerging area.},
added-at = {2011-06-17T14:09:15.000+0200},
author = {Dagan, Ido and Dolan, Bill and Magnini, Bernardo and Roth, Dan},
biburl = {https://www.bibsonomy.org/bibtex/22d2146bdda1f46209f466989b8f599de/jennymac},
description = {Cambridge Journals Online - Abstract - Recognizing textual entailment: Rational, evaluation and approaches},
doi = {10.1017/S1351324909990209},
eprint = {http://journals.cambridge.org/article_S1351324909990209},
interhash = {cf08e477ade8b3a86d70287d35d111a7},
intrahash = {2d2146bdda1f46209f466989b8f599de},
journal = {Natural Language Engineering},
keywords = {language natural textual_entailment},
number = {Special Issue 04},
pages = {i-xvii},
timestamp = {2011-06-17T14:09:15.000+0200},
title = {Recognizing textual entailment: Rational, evaluation and approaches},
url = {http://dx.doi.org/10.1017/S1351324909990209},
volume = 15,
year = 2009
}