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000938504 019__ $$a1178999503$$a1182446070$$a1182512475$$a1182837361$$a1182921511$$a1183934423
000938504 020__ $$a9789811555732$$q(electronic book)
000938504 020__ $$a9811555737$$q(electronic book)
000938504 020__ $$z9789811555725
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000938504 0247_ $$a10.1007/978-981-15-5573-2$$2doi
000938504 0247_ $$a10.1007/978-981-15-5
000938504 035__ $$aSP(OCoLC)on1176494182
000938504 035__ $$aSP(OCoLC)1176494182$$z(OCoLC)1178999503$$z(OCoLC)1182446070$$z(OCoLC)1182512475$$z(OCoLC)1182837361$$z(OCoLC)1182921511$$z(OCoLC)1183934423
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000938504 049__ $$aISEA
000938504 050_4 $$aQA76.9.N38
000938504 08204 $$a006.3/5$$223
000938504 1001_ $$aLiu, Zhiyuan,$$eauthor.
000938504 24510 $$aRepresentation learning for natural language processing /$$cZhiyuan Liu, Yankai Lin, Maosong Sun.
000938504 264_1 $$aSingapore :$$bSpringer,$$c2020.
000938504 300__ $$a1 online resource (xxiv, 334 pages) :$$billustrations 
000938504 336__ $$atext$$btxt$$2rdacontent
000938504 337__ $$acomputer$$bc$$2rdamedia
000938504 338__ $$aonline resource$$bcr$$2rdacarrier
000938504 504__ $$aIncludes bibliographical references.
000938504 5050_ $$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.
000938504 506__ $$aAccess limited to authorized users.
000938504 520__ $$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.
000938504 588__ $$aOnline resource; title from PDF title page (SpringerLink, viewed July 23, 2020).
000938504 650_0 $$aNatural language processing (Computer science)
000938504 7001_ $$aLin, Yankai,$$eauthor.
000938504 7001_ $$aSun, Maosong,$$eauthor.
000938504 77608 $$iPrint version: $$z9811555729$$z9789811555725$$w(OCoLC)1151198143
000938504 852__ $$bebk
000938504 85640 $$3SpringerLink$$uhttps://univsouthin.idm.oclc.org/login?url=http://link.springer.com/10.1007/978-981-15-5573-2$$zOnline Access$$91397441.1
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000938504 980__ $$aEBOOK
000938504 980__ $$aBIB
000938504 982__ $$aEbook
000938504 983__ $$aOnline
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