LEADER 04022nam 22007215 450 001 9910409836103321 005 20251113185739.0 010 $a981-15-5573-7 024 7 $a10.1007/978-981-15-5573-2 035 $a(CKB)4100000011325766 035 $a(DE-He213)978-981-15-5573-2 035 $a(MiAaPQ)EBC6420083 035 $a(Au-PeEL)EBL6420083 035 $a(OCoLC)1176494182 035 $a(oapen)https://directory.doabooks.org/handle/20.500.12854/35038 035 $a(MiAaPQ)EBC30592734 035 $a(Au-PeEL)EBL30592734 035 $a(PPN)260301140 035 $a(ODN)ODN0010073103 035 $a(EXLCZ)994100000011325766 100 $a20200703d2020 u| 0 101 0 $aeng 135 $aurnn|008mamaa 181 $ctxt$2rdacontent 182 $cc$2rdamedia 183 $acr$2rdacarrier 200 10$aRepresentation Learning for Natural Language Processing /$fby Zhiyuan Liu, Yankai Lin, Maosong Sun 205 $a1st ed. 2020. 210 1$aSingapore :$cSpringer Nature Singapore :$cImprint: Springer,$d2020. 215 $a1 online resource (XXIV, 334 p. 131 illus., 99 illus. in color.) 225 1 $aComputer Science Series 311 08$a981-15-5572-9 327 $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. 330 $aThis open access book provides an overview of the recent advances in representation learning theory, algorithms and applications for natural language processing (NLP). It is divided into three parts. Part I presents the representation learning techniques for multiple language entries, including words, phrases, sentences and documents. Part II then introduces the representation techniques for those objects that are closely related to NLP, including entity-based world knowledge, sememe-based linguistic knowledge, networks, and cross-modal entries. Lastly, Part III provides open resource tools for representation learning techniques, and discusses the remaining challenges and future research directions. The theories and algorithms of representation learning presented can also benefit other related domains such as machine learning, social network analysis, semantic Web, information retrieval, data mining and computational biology. This book is intended for advanced undergraduate and graduate students, post-doctoral fellows, researchers, lecturers, and industrial engineers, as well as anyone interested in representation learning and natural language processing. 410 0$aComputer Science Series 606 $aNatural language processing (Computer science) 606 $aComputational linguistics 606 $aArtificial intelligence 606 $aData mining 606 $aNatural Language Processing (NLP) 606 $aComputational Linguistics 606 $aArtificial Intelligence 606 $aData Mining and Knowledge Discovery 615 0$aNatural language processing (Computer science). 615 0$aComputational linguistics. 615 0$aArtificial intelligence. 615 0$aData mining. 615 14$aNatural Language Processing (NLP). 615 24$aComputational Linguistics. 615 24$aArtificial Intelligence. 615 24$aData Mining and Knowledge Discovery. 676 $a006.35 686 $aCOM004000$aCOM021030$aCOM073000$aLAN009000$2bisacsh 700 $aLiu$b Zhiyuan$4aut$4http://id.loc.gov/vocabulary/relators/aut$0851460 702 $aLin$b Yankai$4aut$4http://id.loc.gov/vocabulary/relators/aut 702 $aSun$b Maosong$4aut$4http://id.loc.gov/vocabulary/relators/aut 801 0$bMiAaPQ 801 1$bMiAaPQ 801 2$bMiAaPQ 906 $aBOOK 912 $a9910409836103321 996 $aRepresentation Learning for Natural Language Processing$91900993 997 $aUNINA