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Representation Learning for Natural Language Processing

02/07/2021

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by Zhiyuan Liu, et al.

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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.

Zhiyuan Liu
209 publications
Yankai Lin
59 publications
Maosong Sun
159 publications

∙ 05/22/2023

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Multimodal Representation Learning With Text and Images

In recent years, multimodal AI has seen an upward trend as researchers a...

0 Aishwarya Jayagopal, et al. ∙

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Combining Representation Learning with Logic for Language Processing

The current state-of-the-art in many natural language processing and aut...

0 Tim Rocktäschel, et al. ∙

∙ 03/15/2022

Categorical Representation Learning and RG flow operators for algorithmic classifiers

Following the earlier formalism of the categorical representation learni...

0 Artan Sheshmani, et al. ∙

∙ 03/22/2019

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

The unsupervised text clustering is one of the major tasks in natural la...

0 Jie Zhou, et al. ∙

∙ 02/02/2017

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

Natural language and symbols are intimately correlated. Recent advances ...

0 Lorenzo Ferrone, et al. ∙

∙ 11/26/2019

Representation Learning: A Statistical Perspective

Learning representations of data is an important problem in statistics a...

33 Jianwen Xie, et al. ∙

Code Repositories

RL4NLP

Representation Learning for Natural Language Processing include word ,sentence and document

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