000 04396nam a22005535i 4500
001 978-3-031-02148-0
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005 20240730164323.0
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008 220607s2013 sz | s |||| 0|eng d
020 _a9783031021480
_9978-3-031-02148-0
024 7 _a10.1007/978-3-031-02148-0
_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
_983846
245 1 0 _aSemantic Relations Between Nominals
_h[electronic resource] /
_cby Vivi Nastase, Preslav Nakov, Diarmuid Ó Séaghdha, Stan Szpakowicz.
250 _a1st ed. 2013.
264 1 _aCham :
_bSpringer International Publishing :
_bImprint: Springer,
_c2013.
300 _aXII, 107 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 _aIntroduction -- Relations between Nominals, Relations between Concepts -- Extracting Semantic Relations with Supervision -- Extracting Semantic Relations with Little or No Supervision -- Conclusion.
520 _aPeople make sense of a text by identifying the semantic relations which connect the entities or concepts described by that text. A system which aspires to human-like performance must also be equipped to identify, and learn from, semantic relations in the texts it processes. Understanding even a simple sentence such as "Opportunity and Curiosity find similar rocks on Mars" requires recognizing relations (rocks are located on Mars, signalled by the word on) and drawing on already known relations (Opportunity and Curiosity are instances of the class of Mars rovers). A language-understanding system should be able to find such relations in documents and progressively build a knowledge base or even an ontology. Resources of this kind assist continuous learning and other advanced language-processing tasks such as text summarization, question answering and machine translation. The book discusses the recognition in text of semantic relations which capture interactions between base noun phrases.After a brief historical background, we introduce a range of relation inventories of varying granularity, which have been proposed by computational linguists. There is also variation in the scale at which systems operate, from snippets all the way to the whole Web, and in the techniques of recognizing relations in texts, from full supervision through weak or distant supervision to self-supervised or completely unsupervised methods. A discussion of supervised learning covers available datasets, feature sets which describe relation instances, and successful algorithms. An overview of weakly supervised and unsupervised learning zooms in on the acquisition of relations from large corpora with hardly any annotated data. We show how bootstrapping from seed examples or patterns scales up to very large text collections on the Web. We also present machine learning techniques in which data redundancy and variability lead to fast and reliable relation extraction.
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 _aNakov, Preslav.
_eauthor.
_4aut
_4http://id.loc.gov/vocabulary/relators/aut
_983850
700 1 _aSéaghdha, Diarmuid Ó.
_eauthor.
_4aut
_4http://id.loc.gov/vocabulary/relators/aut
_983852
700 1 _aSzpakowicz, Stan.
_eauthor.
_4aut
_4http://id.loc.gov/vocabulary/relators/aut
_983853
710 2 _aSpringerLink (Online service)
_983856
773 0 _tSpringer Nature eBook
776 0 8 _iPrinted edition:
_z9783031010200
776 0 8 _iPrinted edition:
_z9783031032769
830 0 _aSynthesis Lectures on Human Language Technologies,
_x1947-4059
_983857
856 4 0 _uhttps://doi.org/10.1007/978-3-031-02148-0
912 _aZDB-2-SXSC
942 _cEBK
999 _c85570
_d85570