2016/11/20 by Yoav Goldberg · 4 citations
Computer Science · #Neural Networks and Applications #Topic Modeling
paper · pdf · doi:10.1613/jair.4992
openalex publication_date 2016/11/20 · openalex created_date 2025/10/10 · openalex updated_date 2026/07/28
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.