Representation Learning for Natural Language Processing

Representation Learning for Natural Language Processing

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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...

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Bibliographic Details
Main Author: Liu, Zhiyuan (auth)
Other Authors: Lin, Yankai (auth), Sun, Maosong (auth)
Format: Electronic Book Chapter
Language:English
Published: Springer Nature 2020
Subjects:
Natural language & machine translation
Computational linguistics
Artificial intelligence
Data mining
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
Expert systems / knowledge-based systems
Online Access:OAPEN Library: download the publication
OAPEN Library: description of the publication
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Description
Summary: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.
Physical Description:1 electronic resource (334 p.)
ISBN:978-981-15-5573-2
Access:Open Access

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