Representation Learning for Natural Language Processing

TL;DR

This paper reviews recent advances in distributed representation learning for NLP, discussing its benefits, applications, and remaining challenges to improve natural language processing tasks.

Contribution

It provides a comprehensive overview of the state-of-the-art in representation learning for NLP, highlighting key developments and open challenges.

Findings

Distributed representations enhance NLP performance.

Representation learning is integral to many NLP tasks.

Several challenges remain in effectively applying distributed representations.

Abstract

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.