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008			220601s2021 sz | s |||| 0|eng d
020			_a9783031021787
024	7		_a10.1007/978-3-031-02178-7 _2doi
040			_aES-MaUEC _bspa _cES-MaUEC _dES-MaUEC
050		4	_aP98.5.S45 _b2021 EB
100	1		_aNastase, Vivi, _eautor _4aut _4http://id.loc.gov/vocabulary/relators/aut _9687451 _d1975-
245	1	0	_aSemantic Relations Between Nominal _cby Vivi Nastase, Stan Szpakowicz, Preslav Nakov, Diarmuid Ó Séagdha
250			_a1st edition 2021
264		1	_aCham _bSpringer International Publishing _c2021
300			_a1 recurso en línea (XVI, 220 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		_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.
988			_aSynthesis Collection of Technology_2021
650		7	_2embne _9140755 _aGramática comparada
650		7	_2embne _9666075 _aLingüística computacional
650		7	_2embne _9140153 _aSemántica
700	1		_aSzpakowicz, Stanisław _eautor _4aut _4http://id.loc.gov/vocabulary/relators/aut _9687452
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
776	0	8	_iPrinted edition: _z9783031001895
776	0	8	_iPrinted edition: _z9783031010507
776	0	8	_iPrinted edition: _z9783031033063
856	4	0	_uhttps://go.openathens.net/redirector/universidadeuropea.es?url=https://doi.org/10.1007/978-3-031-02178-7 _zAcceso a este recurso digital (usuarios Universidad Europea de Madrid)
942			_2lcc _cLE
998			_b03/2023 _dz _esc _zSI