Representation Learning for Natural Language Processing

Representation Learning for Natural Language Processing_cover.jpg

Dublin Core

Title

Representation Learning for Natural Language Processing

Subject

Computational linguistics
Data mining
Artificial intelligence
Natural language & machine translation

Description

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.

Creator

Liu, Zhiyuan

Lin, Yankai
Sun, Maosong

Source

https://library.oapen.org/bitstream/20.500.12657/39974/1/2020_Book_RepresentationLearningForNatur.pdf

Publisher

Springer Nature

Date

2020

Rights

http://creativecommons.org/licenses/by/4.0/

Format

Pdf

Language

English

Type

Book

Identifier

10.1007/978-981-15-5573-2

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