A primer on neural network models for natural language processing

Research output: Contribution to journalArticlepeer-review

638 Scopus citations


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.

Original languageEnglish
Pages (from-to)345-420
Number of pages76
JournalJournal of Artificial Intelligence Research
StatePublished - Nov 2016

Bibliographical note

Publisher Copyright:
© 2016 AI Access Foundation. All rights reserved.


Dive into the research topics of 'A primer on neural network models for natural language processing'. Together they form a unique fingerprint.

Cite this