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  • 1
    ISBN: 9789811000805 , 9811000808
    Language: English
    Pages: 1 Online-Ressource (XIV, 242 Seiten) , 76 illus. in color.
    Edition: 1st ed. 2015
    Series Statement: Communications in Computer and Information Science 568
    Parallel Title: Erscheint auch als Social Media Processing
    DDC: 006.3
    Keywords: Artificial intelligence ; Data mining ; Information storage and retrieval systems ; Computer science ; Social sciences—Data processing ; Artificial Intelligence ; Data Mining and Knowledge Discovery ; Information Storage and Retrieval ; Theory of Computation ; Computer Application in Social and Behavioral Sciences
    Library Location Call Number Volume/Issue/Year Availability
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  • 2
    ISBN: 9789819916009 , 9789819915996
    Language: Undetermined
    Pages: 1 Online-Ressource (521 p.)
    Keywords: Natural language & machine translation ; Computational linguistics ; Artificial intelligence ; Data mining
    Abstract: This book provides an overview of the recent advances in representation learning theory, algorithms, and applications for natural language processing (NLP), ranging from word embeddings to pre-trained language models. It is divided into four parts. Part I presents the representation learning techniques for multiple language entries, including words, sentences and documents, as well as pre-training techniques. Part II then introduces the related representation techniques to NLP, including graphs, cross-modal entries, and robustness. Part III then introduces the representation techniques for the knowledge that are closely related to NLP, including entity-based world knowledge, sememe-based linguistic knowledge, legal domain knowledge and biomedical domain knowledge. Lastly, Part IV 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. As compared to the first edition, the second edition (1) provides a more detailed introduction to representation learning in Chapter 1; (2) adds four new chapters to introduce pre-trained language models, robust representation learning, legal knowledge representation learning and biomedical knowledge representation learning; (3) updates recent advances in representation learning in all chapters; and (4) corrects some errors in the first edition. The new contents will be approximately 50%+ compared to the first edition. This is an open access book
    Note: English
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  • 3
    Online Resource
    Online Resource
    [Erscheinungsort nicht ermittelbar] : Springer Nature
    ISBN: 9789811555732
    Language: English
    Pages: 1 Online-Ressource (334 p.)
    Keywords: Natural language & machine translation ; Computational linguistics ; Artificial intelligence ; Data mining
    Abstract: 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
    Note: English
    Library Location Call Number Volume/Issue/Year Availability
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  • 4
    Online Resource
    Online Resource
    [Erscheinungsort nicht ermittelbar] : Springer Nature
    Language: English
    Pages: 1 Online-Ressource (334 p.)
    Keywords: Natural language & machine translation ; Computational linguistics ; Artificial intelligence ; Data mining
    Abstract: 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
    Note: English
    Library Location Call Number Volume/Issue/Year Availability
    BibTip Others were also interested in ...
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