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020 _a9783031021510
_9978-3-031-02151-0
024 7 _a10.1007/978-3-031-02151-0
_2doi
050 4 _aQ334-342
050 4 _aTA347.A78
072 7 _aUYQ
_2bicssc
072 7 _aCOM004000
_2bisacsh
072 7 _aUYQ
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082 0 4 _a006.3
_223
100 1 _aDagan, Ido.
_eauthor.
_4aut
_4http://id.loc.gov/vocabulary/relators/aut
_987799
245 1 0 _aRecognizing Textual Entailment
_h[electronic resource] :
_bModels and Applications /
_cby Ido Dagan, Dan Roth, Fabio Zanzotto, Mark Sammons.
250 _a1st ed. 2013.
264 1 _aCham :
_bSpringer International Publishing :
_bImprint: Springer,
_c2013.
300 _aXX, 204 p.
_bonline resource.
336 _atext
_btxt
_2rdacontent
337 _acomputer
_bc
_2rdamedia
338 _aonline resource
_bcr
_2rdacarrier
347 _atext file
_bPDF
_2rda
490 1 _aSynthesis Lectures on Human Language Technologies,
_x1947-4059
505 0 _aList of Figures -- List of Tables -- Preface -- Acknowledgments -- Textual Entailment -- Architectures and Approaches -- Alignment, Classification, and Learning -- Case Studies -- Knowledge Acquisition for Textual Entailment -- Research Directions in RTE -- Bibliography -- Authors' Biographies.
520 _aIn the last few years, a number of NLP researchers have developed and participated in the task of Recognizing Textual Entailment (RTE). This task encapsulates Natural Language Understanding capabilities within a very simple interface: recognizing when the meaning of a text snippet is contained in the meaning of a second piece of text. This simple abstraction of an exceedingly complex problem has broad appeal partly because it can be conceived also as a component in other NLP applications, from Machine Translation to Semantic Search to Information Extraction. It also avoids commitment to any specific meaning representation and reasoning framework, broadening its appeal within the research community. This level of abstraction also facilitates evaluation, a crucial component of any technological advancement program. This book explains the RTE task formulation adopted by the NLP research community, and gives a clear overview of research in this area. It draws out commonalities in this research, detailing the intuitions behind dominant approaches and their theoretical underpinnings. This book has been written with a wide audience in mind, but is intended to inform all readers about the state of the art in this fascinating field, to give a clear understanding of the principles underlying RTE research to date, and to highlight the short- and long-term research goals that will advance this technology.
650 0 _aArtificial intelligence.
_93407
650 0 _aNatural language processing (Computer science).
_94741
650 0 _aComputational linguistics.
_96146
650 1 4 _aArtificial Intelligence.
_93407
650 2 4 _aNatural Language Processing (NLP).
_931587
650 2 4 _aComputational Linguistics.
_96146
700 1 _aRoth, Dan.
_eauthor.
_4aut
_4http://id.loc.gov/vocabulary/relators/aut
_987801
700 1 _aZanzotto, Fabio.
_eauthor.
_4aut
_4http://id.loc.gov/vocabulary/relators/aut
_987803
700 1 _aSammons, Mark.
_eauthor.
_4aut
_4http://id.loc.gov/vocabulary/relators/aut
_987804
710 2 _aSpringerLink (Online service)
_987806
773 0 _tSpringer Nature eBook
776 0 8 _iPrinted edition:
_z9783031010231
776 0 8 _iPrinted edition:
_z9783031032790
830 0 _aSynthesis Lectures on Human Language Technologies,
_x1947-4059
_987807
856 4 0 _uhttps://doi.org/10.1007/978-3-031-02151-0
912 _aZDB-2-SXSC
942 _cEBK
999 _c86152
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