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Judul Representation Learning for Natural Language Processing / Zhiyuan Liu, Yankai Lin, Maosong Sun
Pengarang Liu, Zhiyuan
Lin, Yankai
Sun, Maosong
Penerbitan Singapore : Springer, 2020
Deskripsi Fisik 349p. :ill.
ISBN 978-981-15-5573-2
Subjek ARTIFICIAL INTELLIGENCE, COMPUTATIONAL LINGUISTICS, DATA MINING AND KNOWLEDGE DISCOVERY
Catatan 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-doctora
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Lokasi Akses Online https://oer.unair.ac.id/files/original/a41802d0be83428551bd184df43863fc.pdf

 
No Barcode No. Panggil Akses Lokasi Ketersediaan
047725192 006.352 3 Liu r Baca Online Perpustakaan Pusat - Online Resources
Ebook
Tersedia
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100 0 # $a Liu, Zhiyuan
245 1 # $a Representation Learning for Natural Language Processing /$c Zhiyuan Liu, Yankai Lin, Maosong Sun
260 # # $a Singapore :$b Springer,$c 2020
300 # # $a 349p. : $b ill.
505 # # $a 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.
650 # # $a ARTIFICIAL INTELLIGENCE, COMPUTATIONAL LINGUISTICS, DATA MINING AND KNOWLEDGE DISCOVERY
700 0 # $a Lin, Yankai
700 0 # $a Sun, Maosong
856 # # $a https://oer.unair.ac.id/files/original/a41802d0be83428551bd184df43863fc.pdf
990 # # $a 047725192
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