000			04108nam a2200445 i 4500
999			_c387990 _d387990 _x1
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008			220607s2013 sz | o |||| 0|eng d
020			_a9783031021480
024	7		_a10.1007/978-3-031-02148-0 _2doi
040			_aES-MaUEC _bspa _cES-MaUEC _dES-MaUEC
050		4	_aP98.5 .S45 _b2013 EB
100	1		_aNastase, Vivi, _d1975- _eautor _4aut _4http://id.loc.gov/vocabulary/relators/aut _9687451
245	1	0	_aSemantic Relations Between Nominals _cby Vivi Nastase, Preslav Nakov, Diarmuid Ó Séaghdha, Stan Szpakowicz
250			_a1st edition 2013
264		1	_aCham _bSpringer International Publishing _c2013
300			_a1 recurso en línea (XII, 107 páginas)
336			_aTexto _btxt _2rdacontent
337			_aelectrónico _bc _2rdamedia
338			_arecurso electrónico _bcr _2rdacarrier
347			_aarchivo de texto _bPDF
490	0		_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.
988			_aSynthesis Collection of Technology_2013
650		7	_2embne _9140153 _aSemántica
650		7	_2embne _9666075 _aLingüística computacional
700	1		_aNakov, Preslav _eautor _4aut _4http://id.loc.gov/vocabulary/relators/aut _9687453
700	1		_aSéagdha, Diarmuid Ó. _eautor _4aut _4http://id.loc.gov/vocabulary/relators/aut _9687454
700	1		_aSzpakowicz, Stanisław _eautor _4aut _4http://id.loc.gov/vocabulary/relators/aut _9687452
776	0	8	_iPrinted edition: _z9783031010200
776	0	8	_iPrinted edition: _z9783031032769
856	4	0	_uhttps://go.openathens.net/redirector/universidadeuropea.es?url=https://doi.org/10.1007/978-3-031-02148-0 _zAcceso a este recurso digital (usuarios Universidad Europea de Madrid)
942			_2lcc _cLE
998			_b05/2023 _dz _eb _zSI