Representation Learning for Natural Language Processing

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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Autores:
Tipo de recurso:
Book
Fecha de publicación:
2020
Institución:
Universidad de Bogotá Jorge Tadeo Lozano
Repositorio:
Expeditio: repositorio UTadeo
Idioma:
eng
OAI Identifier:
oai:expeditiorepositorio.utadeo.edu.co:20.500.12010/14320
Acceso en línea:
https://www.springer.com/gp/book/9789811555725#otherversion=9789811555732
http://hdl.handle.net/20.500.12010/14320
https://doi.org/10.1007/978-981-15-5573-2
Palabra clave:
Computer Science
Linguistics
Natural Language Processing (NLP)
Data Mining and knowledge discovery
Knowledge representation
Word representation
Machine learning
Expert systems -- knowledge -- based systems
Artificial intelligence
Deep learning
Natural language processing
Document representation
Natural language & machine translation
Computational linguistics
Open access
Data mining
Big Data
Rights
License
Abierto (Texto Completo)
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.