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001 978-3-319-47241-6
003 DE-He213
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007 cr nn 008mamaa
008 161018s2017 sz | s |||| 0|eng d
020 _a9783319472416
_9978-3-319-47241-6
024 7 _a10.1007/978-3-319-47241-6
_2doi
050 4 _aQ342
072 7 _aUYQ
_2bicssc
072 7 _aTEC009000
_2bisacsh
072 7 _aUYQ
_2thema
082 0 4 _a006.3
_223
100 1 _aDerczynski, Leon R.A.
_eauthor.
_4aut
_4http://id.loc.gov/vocabulary/relators/aut
_957204
245 1 0 _aAutomatically Ordering Events and Times in Text
_h[electronic resource] /
_cby Leon R.A. Derczynski.
250 _a1st ed. 2017.
264 1 _aCham :
_bSpringer International Publishing :
_bImprint: Springer,
_c2017.
300 _aXXI, 205 p. 25 illus.
_bonline resource.
336 _atext
_btxt
_2rdacontent
337 _acomputer
_bc
_2rdamedia
338 _aonline resource
_bcr
_2rdacarrier
347 _atext file
_bPDF
_2rda
490 1 _aStudies in Computational Intelligence,
_x1860-9503 ;
_v677
505 0 _aIntroduction -- Events and Times -- Temporal Relations -- Relation Labelling Analysis -- Using Temporal Signals -- Using a Framework of Tense and Aspect -- Conclusion.
520 _aThe book offers a detailed guide to temporal ordering, exploring open problems in the field and providing solutions and extensive analysis. It addresses the challenge of automatically ordering events and times in text. Aided by TimeML, it also describes and presents concepts relating to time in easy-to-compute terms. Working out the order that events and times happen has proven difficult for computers, since the language used to discuss time can be vague and complex. Mapping out these concepts for a computational system, which does not have its own inherent idea of time, is, unsurprisingly, tough. Solving this problem enables powerful systems that can plan, reason about events, and construct stories of their own accord, as well as understand the complex narratives that humans express and comprehend so naturally. This book presents a theory and data-driven analysis of temporal ordering, leading to the identification of exactly what is difficult about the task. It then proposes and evaluates machine-learning solutions for the major difficulties. It is a valuable resource for those working in machine learning for natural language processing as well as anyone studying time in language, or involved in annotating the structure of time in documents.
650 0 _aComputational intelligence.
_97716
650 0 _aArtificial intelligence.
_93407
650 0 _aNatural language processing (Computer science).
_94741
650 0 _aComputational linguistics.
_96146
650 1 4 _aComputational Intelligence.
_97716
650 2 4 _aArtificial Intelligence.
_93407
650 2 4 _aNatural Language Processing (NLP).
_931587
650 2 4 _aComputational Linguistics.
_96146
710 2 _aSpringerLink (Online service)
_957205
773 0 _tSpringer Nature eBook
776 0 8 _iPrinted edition:
_z9783319472409
776 0 8 _iPrinted edition:
_z9783319472423
776 0 8 _iPrinted edition:
_z9783319836881
830 0 _aStudies in Computational Intelligence,
_x1860-9503 ;
_v677
_957206
856 4 0 _uhttps://doi.org/10.1007/978-3-319-47241-6
912 _aZDB-2-ENG
912 _aZDB-2-SXE
942 _cEBK
999 _c79895
_d79895