Overview of Modern Deep Learning Techniques Applied to Natural Language Processing
MIT License
This project contains an overview of recent trends in deep learning based natural language processing (NLP). It covers the theoretical descriptions and implementation details behind deep learning models, such as recurrent neural networks (RNNs), convolutional neural networks (CNNs), and reinforcement learning, used to solve various NLP tasks and applications. The overview also contains a summary of state of the art results for NLP tasks such as machine translation, question answering, and dialogue systems. You can find the learning resource at the following address: https://nlpoverview.com/. A snapshot of the website is provided below:
The main motivations for this project are as follows:
There are various ways to contribute to this project.
fork
the repo, browse to the corresponding chapter
, and then click on edit
button to add your info. The image below shows the last two steps after you have forked the repo. You can then submit a pull request and we will approve accordingly. If you would like to change a huge portion of the project or even add a chapter, then we recommend looking at the "Build site locally"
section below.git
. We will help edit and revise the content and then further assist you to incorporate the contributions to the project.If you are planning to change some aspect of the site (e.g., adding section or style) and want to preview it locally on your machine, we suggest you to build and run the site locally using jekyll
. Here are the instructions:
Ruby 2.1.0
or higher is installed on your computer. You can check using the ruby --version
command. If not, please install it using the instructions provided here.gem install bundler
.git clone https://github.com/omarsar/nlp_overview.git
cd nlp_overview
bundle install
bundle exec jekyll serve
http://localhost:4000
This project is maintained by Elvis Saravia and Soujanya Poria. You can also find me on Twitter if you have any direct comments or questions. A major part of this project have been directly borrowed from the work of Young et al. (2017). We are thankful to the authors.