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Representation Learning for Natural Language Processing

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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, lecturers, and industrial engineers, as well as anyone interested in representation learning and natural language processing.

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Detail Information

Series Title

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Call Number

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Publisher

: Springer Singapore., 2023

Collation

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Language

English

ISBN/ISSN

978-981-99-1599-6

Classification

NONE

Content Type

text

Media Type

computer

Carrier Type

online resource

Edition

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Subject(s)

Artificial Intelligence
Data Mining and Knowledge Discovery
Natural Language Processing (NLP)
, Computational Linguistics

Specific Detail Info

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Statement of Responsibility

Zhiyuan Liu, Yankai Lin, Maosong Sun

Other Information

Cataloger

reva

Source

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Validator

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Digital Object Identifier (DOI)

https://doi.org/10.1007/978-981-99-1600-9

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