000 | 03506nam a22005655i 4500 | ||
---|---|---|---|
001 | 978-3-319-47241-6 | ||
003 | DE-He213 | ||
005 | 20220801221639.0 | ||
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 |