Representation Learning for Natural Language Processing

by Zhiyuan Liu, Yankai Lin, Maosong Sun

Representation Learning for Natural Language Processing

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Book Description

This open 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.

This open book is licensed under a Creative Commons License (CC BY). You can download Representation Learning for Natural Language Processing ebook for free in PDF format (10.3 MB).

Book Details

Subject
Computer Science
Publisher
Springer
Published
2020
Pages
349
Edition
1
Language
English
ISBN13
9789811555725
ISBN10
9811555729
ISBN13 Digital
9789811555732
ISBN10 Digital
9811555737
PDF Size
10.3 MB
License
CC BY

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