Representation Learning for Natural Language Processing
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 word...
Wedi'i Gadw mewn:
| Prif Awduron: | , , |
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| Fformat: | Online |
| Iaith: | Saesneg |
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Springer Nature
2021
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| Pynciau: | |
| Mynediad Ar-lein: | ONIX_20200714_9789811555732_9 |
| Tagiau: |
Dim Tagiau, Byddwch y cyntaf i dagio'r cofnod hwn!
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| _version_ | 1869527119118204928 |
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| author | Liu, Zhiyuan Lin, Yankai Sun, Maosong |
| author_browse | Lin, Yankai Liu, Zhiyuan Sun, Maosong |
| author_facet | Liu, Zhiyuan Lin, Yankai Sun, Maosong |
| author_sort | Liu, Zhiyuan |
| collection | Directory of Open Access Books |
| description | 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. |
| format | Online |
| id | doab-20.500.12854ir-35038 |
| institution | Directory of Open Access Books |
| language | eng |
| publishDate | 2021 |
| publishDateRange | 2021 |
| publishDateSort | 2021 |
| publisher | Springer Nature |
| publisherStr | Springer Nature |
| record_format | ojs |
| spelling | doab-20.500.12854ir-350382025-03-12T00:58:20Z Representation Learning for Natural Language Processing Liu, Zhiyuan Lin, Yankai Sun, Maosong Natural Language Processing (NLP) Computational Linguistics Artificial Intelligence Data Mining and Knowledge Discovery Open Access Deep Learning Representation Learning Knowledge Representation Word Representation Document Representation Big Data Machine Learning Natural Language Processing Natural language & machine translation Computational linguistics Artificial intelligence Data mining Expert systems / knowledge-based systems thema EDItEUR::U Computing and Information Technology::UY Computer science::UYQ Artificial intelligence::UYQL Natural language and machine translation thema EDItEUR::C Language and Linguistics::CF Linguistics::CFX Computational and corpus linguistics thema EDItEUR::U Computing and Information Technology::UY Computer science::UYQ Artificial intelligence thema EDItEUR::U Computing and Information Technology::UN Databases::UNF Data mining 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. 2021-02-10T14:22:46Z 2021-02-10T14:22:46Z 2020-07-14T07:18:21Z 2020 book ONIX_20200714_9789811555732_9 OCN: 1176494182 https://library.oapen.org/handle/20.500.12657/39974 https://directory.doabooks.org/handle/20.500.12854/35038 eng open access image/jpeg image/jpeg image/jpeg n/a n/a n/a https://library.oapen.org/bitstream/20.500.12657/39974/1/2020_Book_RepresentationLearningForNatur.pdf https://library.oapen.org/bitstream/20.500.12657/39974/1/2020_Book_RepresentationLearningForNatur.pdf https://library.oapen.org/bitstream/20.500.12657/39974/1/2020_Book_RepresentationLearningForNatur.pdf Springer Nature Springer 10.1007/978-981-15-5573-2 10.1007/978-981-15-5573-2 9fa3421d-f917-4153-b9ab-fc337c396b5a Springer 334 open access |
| spellingShingle | Natural Language Processing (NLP) Computational Linguistics Artificial Intelligence Data Mining and Knowledge Discovery Open Access Deep Learning Representation Learning Knowledge Representation Word Representation Document Representation Big Data Machine Learning Natural Language Processing Natural language & machine translation Computational linguistics Artificial intelligence Data mining Expert systems / knowledge-based systems thema EDItEUR::U Computing and Information Technology::UY Computer science::UYQ Artificial intelligence::UYQL Natural language and machine translation thema EDItEUR::C Language and Linguistics::CF Linguistics::CFX Computational and corpus linguistics thema EDItEUR::U Computing and Information Technology::UY Computer science::UYQ Artificial intelligence thema EDItEUR::U Computing and Information Technology::UN Databases::UNF Data mining Liu, Zhiyuan Lin, Yankai Sun, Maosong Representation Learning for Natural Language Processing |
| title | Representation Learning for Natural Language Processing |
| title_full | Representation Learning for Natural Language Processing |
| title_fullStr | Representation Learning for Natural Language Processing |
| title_full_unstemmed | Representation Learning for Natural Language Processing |
| title_short | Representation Learning for Natural Language Processing |
| title_sort | representation learning for natural language processing |
| topic | Natural Language Processing (NLP) Computational Linguistics Artificial Intelligence Data Mining and Knowledge Discovery Open Access Deep Learning Representation Learning Knowledge Representation Word Representation Document Representation Big Data Machine Learning Natural Language Processing Natural language & machine translation Computational linguistics Artificial intelligence Data mining Expert systems / knowledge-based systems thema EDItEUR::U Computing and Information Technology::UY Computer science::UYQ Artificial intelligence::UYQL Natural language and machine translation thema EDItEUR::C Language and Linguistics::CF Linguistics::CFX Computational and corpus linguistics thema EDItEUR::U Computing and Information Technology::UY Computer science::UYQ Artificial intelligence thema EDItEUR::U Computing and Information Technology::UN Databases::UNF Data mining |
| topic_facet | Natural Language Processing (NLP) Computational Linguistics Artificial Intelligence Data Mining and Knowledge Discovery Open Access Deep Learning Representation Learning Knowledge Representation Word Representation Document Representation Big Data Machine Learning Natural Language Processing Natural language & machine translation Computational linguistics Artificial intelligence Data mining Expert systems / knowledge-based systems thema EDItEUR::U Computing and Information Technology::UY Computer science::UYQ Artificial intelligence::UYQL Natural language and machine translation thema EDItEUR::C Language and Linguistics::CF Linguistics::CFX Computational and corpus linguistics thema EDItEUR::U Computing and Information Technology::UY Computer science::UYQ Artificial intelligence thema EDItEUR::U Computing and Information Technology::UN Databases::UNF Data mining |
| url | ONIX_20200714_9789811555732_9 |
| work_keys_str_mv | AT liuzhiyuan representationlearningfornaturallanguageprocessing AT linyankai representationlearningfornaturallanguageprocessing AT sunmaosong representationlearningfornaturallanguageprocessing |