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020 _a9783031794742
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024 7 _a10.1007/978-3-031-79474-2
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100 1 _aMaynard, Diana.
_eauthor.
_4aut
_4http://id.loc.gov/vocabulary/relators/aut
_979129
245 1 0 _aNatural Language Processing for the Semantic Web
_h[electronic resource] /
_cby Diana Maynard, Kalina Bontcheva, Isabelle Augenstein.
250 _a1st ed. 2017.
264 1 _aCham :
_bSpringer International Publishing :
_bImprint: Springer,
_c2017.
300 _aXIV, 182 p.
_bonline resource.
336 _atext
_btxt
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337 _acomputer
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_2rdamedia
338 _aonline resource
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347 _atext file
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490 1 _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.
650 0 _aMathematics.
_911584
650 0 _aInternet programming.
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650 0 _aApplication software.
_979130
650 0 _aComputer networks .
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650 0 _aOntology.
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650 1 4 _aMathematics.
_911584
650 2 4 _aWeb Development.
_935505
650 2 4 _aComputer and Information Systems Applications.
_979131
650 2 4 _aComputer Communication Networks.
_979132
650 2 4 _aOntology.
_95277
700 1 _aBontcheva, Kalina.
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_4aut
_4http://id.loc.gov/vocabulary/relators/aut
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700 1 _aAugenstein, Isabelle.
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776 0 8 _iPrinted edition:
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776 0 8 _iPrinted edition:
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776 0 8 _iPrinted edition:
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830 0 _aSynthesis Lectures on Data, Semantics, and Knowledge,
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856 4 0 _uhttps://doi.org/10.1007/978-3-031-79474-2
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