Deep Learning on Graphs for Natural Language Processing Demo
The repository contains code examples for DLG4NLP tutorials at NAACL 2021, SIGIR 2021, KDD 2021, IJCAI 2021, AAAI 2022 and TheWebConf 2022.
Slides can be downloaded from here.
Get Started
You will need to install our graph4nlp library in order to run the demo code. Please follow the following environment setup instructions. Please also refer to the graph4nlp repository page for more details on how to use the library.
Environment setup
- Create virtual environment
conda create --name graph4nlp python=3.8
conda activate graph4nlp
- Install graph4nlp library
- Clone the github repo
git clone -b [branch_version] https://github.com/graph4ai/graph4nlp.git
cd graph4nlp
Please choose the branch version corresponding to the demo version as shown in the table below.
| demo version | library branch version |
|---|---|
| DLG4NLP@ICLR 2022 | v0.5.5 |
| TheWebConf 2022 | v0.5.5 |
| AAAI 2022 | v0.5.5 |
| CLIQ-ai 2021 | stable_nov2021b |
| IJCAI 2021 | stable_202108 |
| KDD 2021 | stable_202108 |
| SIGIR 2021 | stable |
| NAACL 2021 | stable |
- Then run
./configure(or./configure.batif you are using Windows 10) to config your installation. The configuration program will ask you to specify your CUDA version. If you do not have a GPU, please choose 'cpu'.
./configure
- Finally, install the package
python setup.py install
- Install other packages
pip install torchtext
pip install notebook
- Set up StanfordCoreNLP (for static graph construction only, unnecessary for this demo because preprocessed data is provided)
- Download StanfordCoreNLP
- Go to the root folder and start the server
java -mx4g -cp "*" edu.stanford.nlp.pipeline.StanfordCoreNLPServer -port 9000 -timeout 15000
Start Jupyter notebook and run the demo
After complete the above steps, you can start the jupyter notebook server to run the demo:
cd graph4nlp_demo/XYZ
jupyter notebook
Note that you will need to change XYZ to the specific folder name.