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 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.

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Ketersediaan

Informasi Detail

No. Panggil

-

Penerbit

Singapore : Springer Nature., 2020

Bahasa

English

ISBN/ISSN

978-981-15-5573-2

Klasifikasi

-

Edisi

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Subjek

Open access
machine learning
Big Data
artificial intelligence
knowledge representation
Data Mining and Knowledge Discovery
Deep Learning
Representation Learning
Word Representation
Document Representation
Natural Language Processing
Natural language & machine translation
Data mining
Expert systems / knowledge-based systems

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