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008			220601s2017 sz | o |||| 0|eng d
020			_a9783031794742
024	7		_a10.1007/978-3-031-79474-2 _2doi
040			_aES-MaUEC _bspa _cES-MaUEC _dES-MaUEC
050		4	_aQA76.9.N38 _b2017 EB
100	1		_aMaynard, Diana _eautor _4aut _4http://id.loc.gov/vocabulary/relators/aut _9686511
245	1	0	_aNatural Language Processing for the Semantic Web _cby Diana Maynard, Kalina Bontcheva, Isabelle Augenstein
250			_a1st edition 2017
264		1	_aCham _bSpringer International Publishing _c2017
300			_a1 recurso en línea (XIV, 182 páginas)
336			_atexto _btxt _2rdacontent
337			_aelectrónico _bc _2rdamedia
338			_arecurso electrónico _bcr _2rdacarrier
347			_aarchivo de texto _bPDF
490	0		_aSynthesis Lectures on Data Semantics and Knowledge _x2691-2031
505	0		_aAcknowledgments -- Introduction -- Linguistic Processing -- Named Entity Recognition and Classification -- Relation Extraction -- Entity Linking -- Automated Ontology Development -- Sentiment Analysis -- NLP for Social Media -- Applications -- Conclusions -- Bibliography -- Authors' Biographies .
520			_aThis book introduces core natural language processing (NLP) technologies to non-experts in an easily accessible way, as a series of building blocks that lead the user to understand key technologies, why they are required, and how to integrate them into Semantic Web applications. Natural language processing and Semantic Web technologies have different, but complementary roles in data management. Combining these two technologies enables structured and unstructured data to merge seamlessly. Semantic Web technologies aim to convert unstructured data to meaningful representations, which benefit enormously from the use of NLP technologies, thereby enabling applications such as connecting text to Linked Open Data, connecting texts to each other, semantic searching, information visualization, and modeling of user behavior in online networks. The first half of this book describes the basic NLP processing tools: tokenization, part-of-speech tagging, and morphological analysis, in addition to the main tools required for an information extraction system (named entity recognition and relation extraction) which build on these components. The second half of the book explains how Semantic Web and NLP technologies can enhance each other, for example via semantic annotation, ontology linking, and population. These chapters also discuss sentiment analysis, a key component in making sense of textual data, and the difficulties of performing NLP on social media, as well as some proposed solutions. The book finishes by investigating some applications of these tools, focusing on semantic search and visualization, modeling user behavior, and an outlook on the future.
988			_aSynthesis Collection of Technology_2017
650		7	_2embne _9158738 _aProceso en lenguaje natural (Informática)
650		7	_2embne _9163805 _aWeb semántica
700	1		_aBontcheva, Kalina _eautor _4aut _4http://id.loc.gov/vocabulary/relators/aut _9686512
700	1		_aAugenstein, Isabelle _eautor _4aut _4http://id.loc.gov/vocabulary/relators/aut _9686513
776	0	8	_iPrinted edition: _z9783031794759
776	0	8	_iPrinted edition: _z9783031794735
776	0	8	_iPrinted edition: _z9783031794766
856	4	0	_uhttps://go.openathens.net/redirector/universidadeuropea.es?url=https://doi.org/10.1007/978-3-031-79474-2 _zAcceso a este recurso digital (usuarios Universidad Europea de Madrid)
942			_2lcc _cLE
998			_b02/2023 _dz _eb _zSI