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001			978-981-15-5573-2
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008			200703s2020 si | s |||| 0|eng d
020			_a9789811555732 _9978-981-15-5573-2
024	7		_a10.1007/978-981-15-5573-2 _2doi
050		4	_aQA76.9.N38
050		4	_aQA76.9.N38
072		7	_aUYQL _2bicssc
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082	0	4	_a006.35 _223
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100	1		_aLiu, Zhiyuan. _eauthor. _4aut _4http://id.loc.gov/vocabulary/relators/aut
245	1	0	_aRepresentation Learning for Natural Language Processing _h[electronic resource] / _cby Zhiyuan Liu, Yankai Lin, Maosong Sun.
250			_a1st ed. 2020.
264		1	_aSingapore : _bSpringer Singapore : _bImprint: Springer, _c2020.
300			_aXXIV, 334 p. 131 illus., 99 illus. in color. _bonline resource.
336			_atext _btxt _2rdacontent
337			_acomputer _bc _2rdamedia
338			_aonline resource _bcr _2rdacarrier
347			_atext file _bPDF _2rda
505	0		_a1. Representation Learning and NLP -- 2. Word Representation -- 3. Compositional Semantics -- 4. Sentence Representation -- 5. Document Representation -- 6. Sememe Knowledge Representation -- 7. World Knowledge Representation -- 8. Network Representation -- 9. Cross-Modal Representation -- 10. Resources -- 11. Outlook.
506	0		_aOpen Access
520			_aThis open access book provides an overview of the recent advances in representation learning theory, algorithms and applications for natural language processing (NLP). It is divided into three parts. Part I presents the representation learning techniques for multiple language entries, including words, phrases, sentences and documents. Part II then introduces the representation techniques for those objects that are closely related to NLP, including entity-based world knowledge, sememe-based linguistic knowledge, networks, and cross-modal entries. Lastly, Part III provides open resource tools for representation learning techniques, and discusses the remaining challenges and future research directions. The theories and algorithms of representation learning presented can also benefit other related domains such as machine learning, social network analysis, semantic Web, information retrieval, data mining and computational biology. This book is intended for advanced undergraduate and graduate students, post-doctoral fellows, researchers, lecturers, and industrial engineers, as well as anyone interested in representation learning and natural language processing.
650		0	_aNatural language processing (Computer science).
650		0	_aComputational linguistics.
650		0	_aArtificial intelligence.
650		0	_aData mining.
650	1	4	_aNatural Language Processing (NLP). _0https://scigraph.springernature.com/ontologies/product-market-codes/I21040
650	2	4	_aComputational Linguistics. _0https://scigraph.springernature.com/ontologies/product-market-codes/N22000
650	2	4	_aArtificial Intelligence. _0https://scigraph.springernature.com/ontologies/product-market-codes/I21000
650	2	4	_aNatural Language Processing (NLP). _0https://scigraph.springernature.com/ontologies/product-market-codes/I21040
650	2	4	_aData Mining and Knowledge Discovery. _0https://scigraph.springernature.com/ontologies/product-market-codes/I18030
700	1		_aLin, Yankai. _eauthor. _4aut _4http://id.loc.gov/vocabulary/relators/aut
700	1		_aSun, Maosong. _eauthor. _4aut _4http://id.loc.gov/vocabulary/relators/aut
710	2		_aSpringerLink (Online service)
773	0		_tSpringer Nature eBook
776	0	8	_iPrinted edition: _z9789811555725
776	0	8	_iPrinted edition: _z9789811555749
776	0	8	_iPrinted edition: _z9789811555756
856	4	0	_uhttps://doi.org/10.1007/978-981-15-5573-2
912			_aZDB-2-SCS
912			_aZDB-2-SXCS
912			_aZDB-2-SOB
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