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dc.contributor.authorLiu, Zhiyuan
dc.contributor.authorLin, Yankai
dc.contributor.authorSun, Maosong
dc.date.accessioned2021-02-10T14:22:46Z
dc.date.available2021-02-10T14:22:46Z
dc.date.issued2020
dc.identifierONIX_20200714_9789811555732_9
dc.identifier46548*
dc.identifierhttps://library.oapen.org/handle/20.500.12657/39974
dc.identifier.urihttps://directory.doabooks.org/handle/20.500.12854/35038
dc.description.abstractThis 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.
dc.languageEnglish
dc.rightsopen access
dc.subject.classificationbic Book Industry Communication::U Computing & information technology::UY Computer science::UYQ Artificial intelligence::UYQL Natural language & machine translation
dc.subject.classificationbic Book Industry Communication::C Language::CF linguistics::CFX Computational linguistics
dc.subject.classificationbic Book Industry Communication::U Computing & information technology::UY Computer science::UYQ Artificial intelligence
dc.subject.classificationbic Book Industry Communication::U Computing & information technology::UN Databases::UNF Data mining
dc.subject.otherNatural Language Processing (NLP)
dc.subject.otherComputational Linguistics
dc.subject.otherArtificial Intelligence
dc.subject.otherData Mining and Knowledge Discovery
dc.subject.otherOpen Access
dc.subject.otherDeep Learning
dc.subject.otherRepresentation Learning
dc.subject.otherKnowledge Representation
dc.subject.otherWord Representation
dc.subject.otherDocument Representation
dc.subject.otherBig Data
dc.subject.otherMachine Learning
dc.subject.otherNatural Language Processing
dc.subject.otherNatural language & machine translation
dc.subject.otherComputational linguistics
dc.subject.otherArtificial intelligence
dc.subject.otherData mining
dc.subject.otherExpert systems / knowledge-based systems
dc.titleRepresentation Learning for Natural Language Processing
dc.typebook
oapen.identifier.doi10.1007/978-981-15-5573-2
oapen.relation.isPublishedBy9fa3421d-f917-4153-b9ab-fc337c396b5a
oapen.imprintSpringer
oapen.pages334
dc.dateSubmitted2020-07-14T07:18:21Z


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Except where otherwise noted, this item's license is described as open access