01163nam--2200397---450-99000139938020331620051103123241.0000139938USA01000139938(ALEPH)000139938USA0100013993820040206d1973----km-y0itay0103----bagerDE||||||||001yy<<Die>> interpolationen in der Odysseeeine untersuchungFriedrich BlassRist. anast.Hildesheim [etc.]Olms1973306 p.19 cmRipr. facs. dell'ed.: Halle, 190420012001001-------2001Omero. OdisseaBLASS,Friedrich229990ITsalbcISBD990001399380203316V.1.F. 207(VIII C 1077)67684 L.M.VIII CBKUMASIAV51020040206USA011233SIAV51020040206USA011235PATRY9020040406USA011738COPAT39020051103USA011232Interpolationen in der Odyssee519053UNISA03864nam 22006735 450 99646546280331620230612193145.0981-15-5573-710.1007/978-981-15-5573-2(CKB)4100000011325766(DE-He213)978-981-15-5573-2(MiAaPQ)EBC6420083(Au-PeEL)EBL6420083(OCoLC)1176494182(oapen)https://directory.doabooks.org/handle/20.500.12854/35038(MiAaPQ)EBC30592734(Au-PeEL)EBL30592734(PPN)260301140(EXLCZ)99410000001132576620200703d2020 u| 0engurnn|008mamaatxtrdacontentcrdamediacrrdacarrierRepresentation Learning for Natural Language Processing[electronic resource] /by Zhiyuan Liu, Yankai Lin, Maosong Sun1st ed. 2020.Singapore :Springer Nature Singapore :Imprint: Springer,2020.1 online resource (XXIV, 334 p. 131 illus., 99 illus. in color.) 981-15-5572-9 1. 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.This 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.Natural language processing (Computer science)Computational linguisticsArtificial intelligenceData miningNatural Language Processing (NLP)Computational LinguisticsArtificial IntelligenceData Mining and Knowledge DiscoveryNatural language processing (Computer science).Computational linguistics.Artificial intelligence.Data mining.Natural Language Processing (NLP).Computational Linguistics.Artificial Intelligence.Data Mining and Knowledge Discovery.006.35Liu Zhiyuanauthttp://id.loc.gov/vocabulary/relators/aut851460Lin Yankaiauthttp://id.loc.gov/vocabulary/relators/autSun Maosongauthttp://id.loc.gov/vocabulary/relators/autMiAaPQMiAaPQMiAaPQBOOK996465462803316Representation Learning for Natural Language Processing1900993UNISA