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020 _a9783031021787
_9978-3-031-02178-7
024 7 _a10.1007/978-3-031-02178-7
_2doi
050 4 _aQ334-342
050 4 _aTA347.A78
072 7 _aUYQ
_2bicssc
072 7 _aCOM004000
_2bisacsh
072 7 _aUYQ
_2thema
082 0 4 _a006.3
_223
100 1 _aNastase, Vivi.
_eauthor.
_4aut
_4http://id.loc.gov/vocabulary/relators/aut
_980711
245 1 0 _aSemantic Relations Between Nominals, Second Edition
_h[electronic resource] /
_cby Vivi Nastase, Stan Szpakowicz, Preslav Nakov, Diarmuid Ó Séagdha.
250 _a2nd ed. 2021.
264 1 _aCham :
_bSpringer International Publishing :
_bImprint: Springer,
_c2021.
300 _aXVI, 220 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 _aPreface to the Second Edition -- Introduction -- Relations Between Nominals, Relations Between Concepts -- Extracting Semantic Relations with Supervision -- Extracting Semantic Relations with Little or No Supervision -- Semantic Relations and Deep Learning -- Conclusion -- Bibliography -- Authors' Biographies -- Index.
520 _aOpportunity and Curiosity find similar rocks on Mars. One can generally understand this statement if one knows that Opportunity and Curiosity are instances of the class of Mars rovers, and recognizes that, as signalled by the word on, rocks are located on Mars. Two mental operations contribute to understanding: recognize how entities/concepts mentioned in a text interact and recall already known facts (which often themselves consist of relations between entities/concepts). Concept interactions one identifies in the text can be added to the repository of known facts, and aid the processing of future texts. The amassed knowledge can assist many advanced language-processing tasks, including summarization, question answering and machine translation. Semantic relations are the connections we perceive between things which interact. The book explores two, now intertwined, threads in semantic relations: how they are expressed in texts and what role they play in knowledge repositories. A historical perspective takes us back more than 2000 years to their beginnings, and then to developments much closer to our time: various attempts at producing lists of semantic relations, necessary and sufficient to express the interaction between entities/concepts. A look at relations outside context, then in general texts, and then in texts in specialized domains, has gradually brought new insights, and led to essential adjustments in how the relations are seen. At the same time, datasets which encompass these phenomena have become available. They started small, then grew somewhat, then became truly large. The large resources are inevitably noisy because they are constructed automatically. The available corpora-to be analyzed, or used to gather relational evidence-have also grown, and some systems now operate at the Web scale. The learning of semantic relations has proceeded in parallel, in adherence to supervised, unsupervised or distantly supervised paradigms. Detailed analyses of annotated datasets in supervised learning have granted insights useful in developing unsupervised and distantly supervised methods. These in turn have contributed to the understanding of what relations are and how to find them, and that has led to methods scalable to Web-sized textual data. The size and redundancy of information in very large corpora, which at first seemed problematic, have been harnessed to improve the process of relation extraction/learning. The newest technology, deep learning, supplies innovative and surprising solutions to a variety of problems in relation learning. This book aims to paint a big picture and to offer interesting details.
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 _aSzpakowicz, Stan.
_eauthor.
_4aut
_4http://id.loc.gov/vocabulary/relators/aut
_980712
700 1 _aNakov, Preslav.
_eauthor.
_4aut
_4http://id.loc.gov/vocabulary/relators/aut
_980713
700 1 _aSéagdha, Diarmuid Ó.
_eauthor.
_4aut
_4http://id.loc.gov/vocabulary/relators/aut
_980714
710 2 _aSpringerLink (Online service)
_980715
773 0 _tSpringer Nature eBook
776 0 8 _iPrinted edition:
_z9783031001895
776 0 8 _iPrinted edition:
_z9783031010507
776 0 8 _iPrinted edition:
_z9783031033063
830 0 _aSynthesis Lectures on Human Language Technologies,
_x1947-4059
_980716
856 4 0 _uhttps://doi.org/10.1007/978-3-031-02178-7
912 _aZDB-2-SXSC
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