A Primer on Neural Network Models for Natural Language Processing

TL;DR

This paper provides a comprehensive tutorial on neural network models tailored for natural language processing, covering various architectures and techniques to help NLP researchers understand and apply neural methods effectively.

Contribution

It offers an accessible overview of neural network models specifically for NLP, bridging the gap between neural techniques and natural language research.

Findings

Neural networks have achieved state-of-the-art results in NLP tasks.

The tutorial covers input encoding, feed-forward, convolutional, recurrent, and recursive networks.

It explains the computation graph abstraction for automatic differentiation.

Abstract

Over the past few years, neural networks have re-emerged as powerful machine-learning models, yielding state-of-the-art results in fields such as image recognition and speech processing. More recently, neural network models started to be applied also to textual natural language signals, again with very promising results. This tutorial surveys neural network models from the perspective of natural language processing research, in an attempt to bring natural-language researchers up to speed with the neural techniques. The tutorial covers input encoding for natural language tasks, feed-forward networks, convolutional networks, recurrent networks and recursive networks, as well as the computation graph abstraction for automatic gradient computation.