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Titlebook: Representation Learning for Natural Language Processing; Zhiyuan Liu,Yankai Lin,Maosong Sun Book‘‘‘‘‘‘‘‘ 2023Latest edition The Editor(s)

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發(fā)表于 2025-3-21 18:35:19 | 只看該作者 |倒序瀏覽 |閱讀模式
書目名稱Representation Learning for Natural Language Processing
編輯Zhiyuan Liu,Yankai Lin,Maosong Sun
視頻videohttp://file.papertrans.cn/828/827394/827394.mp4
概述Provides a comprehensive overview of the representation learning techniques for natural language processing.Presents a self-contained reference resource with a rich blend of theory, algorithms and app
圖書封面Titlebook: Representation Learning for Natural Language Processing;  Zhiyuan Liu,Yankai Lin,Maosong Sun Book‘‘‘‘‘‘‘‘ 2023Latest edition The Editor(s)
描述.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, socialnetwork analysis, semantic Web, information retrieval, data mining and computational biology. This book is intended for advanced undergraduate and graduate students, post-doctoral fellows, researchers, lec
出版日期Book‘‘‘‘‘‘‘‘ 2023Latest edition
關(guān)鍵詞Open Access; Deep Learning; Representation Learning; Knowledge Representation; Word Representation; Docum
版次2
doihttps://doi.org/10.1007/978-981-99-1600-9
isbn_softcover978-981-99-1602-3
isbn_ebook978-981-99-1600-9
copyrightThe Editor(s) (if applicable) and The Author(s) 2023
The information of publication is updating

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