Representation Learning for Natural Language Processing

02/07/2021

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This book aims to review and present the recent advances of distributed representation learning for NLP, including why representation learning can improve NLP, how representation learning takes part in various important topics of NLP, and what challenges are still not well addressed by distributed representation.

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Representation Learning for Natural Language Processing

Beyond Words: A Comprehensive Survey of Sentence Representations

Multimodal Representation Learning With Text and Images

Combining Representation Learning with Logic for Language Processing

Categorical Representation Learning and RG flow operators for algorithmic classifiers

An end-to-end Neural Network Framework for Text Clustering

Symbolic, Distributed and Distributional Representations for Natural Language Processing in the Era of Deep Learning: a Survey

Representation Learning: A Statistical Perspective

Representation Learning for Natural Language Processing

Related Research

Beyond Words: A Comprehensive Survey of Sentence Representations

Multimodal Representation Learning With Text and Images

Combining Representation Learning with Logic for Language Processing

Categorical Representation Learning and RG flow operators for algorithmic classifiers

An end-to-end Neural Network Framework for Text Clustering

Symbolic, Distributed and Distributional Representations for Natural Language Processing in the Era of Deep Learning: a Survey

Representation Learning: A Statistical Perspective