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dc.creatorZhiyuan, Liu
dc.creatorYankai, Lin
dc.creatorMaosong, Sun
dc.date.accessioned2020-10-09T01:32:36Z
dc.date.available2020-10-09T01:32:36Z
dc.date.created2020-07-15
dc.identifier.isbn978-981-15-5573-2
dc.identifier.isbn978-981-15-5572-5
dc.identifier.otherhttps://www.springer.com/gp/book/9789811555725#otherversion=9789811555732
dc.identifier.urihttp://hdl.handle.net/20.500.12010/14320
dc.format.extent349 páginasspa
dc.format.mimetypeapplication/pdfspa
dc.language.isoengspa
dc.publisherSpringer Naturespa
dc.subjectComputer Sciencespa
dc.subjectLinguisticsspa
dc.subjectNatural Language Processing (NLP)spa
dc.subjectData Mining and knowledge discoveryspa
dc.subjectKnowledge representationspa
dc.subjectWord representationspa
dc.subjectMachine learningspa
dc.titleRepresentation Learning for Natural Language Processingspa
dc.type.localLibrospa
dc.subject.lembExpert systems -- knowledge -- based systemsspa
dc.subject.lembArtificial intelligencespa
dc.subject.lembDeep learningspa
dc.subject.lembNatural language processingspa
dc.subject.lembDocument representationspa
dc.subject.lembNatural language & machine translationspa
dc.rights.accessrightsinfo:eu-repo/semantics/openAccessspa
dc.rights.localAbierto (Texto Completo)spa
dc.subject.keywordComputational linguisticsspa
dc.subject.keywordOpen accessspa
dc.subject.keywordData miningspa
dc.subject.keywordBig Dataspa
dc.identifier.doihttps://doi.org/10.1007/978-981-15-5573-2
dc.description.abstractenglishThis 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.spa
dc.type.coarhttp://purl.org/coar/resource_type/c_2f33spa
dc.rights.creativecommonshttp://creativecommons.org/licenses/by/4.0/


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