DualGCN
Code and datasets of our paper "Dual Graph Convolutional Networks for Aspect-based Sentiment Analysis" accepted by ACL 2021.
Requirements
- torch==1.4.0
- scikit-learn==0.23.2
- transformers==3.2.0
- cython==0.29.13
- nltk==3.5
To install requirements, run pip install -r requirements.txt.
Preparation
-
Download and unzip GloVe vectors(
glove.840B.300d.zip) from https://nlp.stanford.edu/projects/glove/ and put it intoDualGCN/glovedirectory. -
Prepare vocabulary with:
sh DualGCN/build_vocab.sh -
Download the best model best_parser.pt of LAL-Parser.
Training
To train the DualGCN model, run:
sh DualGCN/run.sh
Credits
The code and datasets in this repository are based on ABSA-PyTorch and CDT_ABSA.
Citation
If you find this work useful, please cite as following.
@inproceedings{li-etal-2021-dual-graph,
title = "Dual Graph Convolutional Networks for Aspect-based Sentiment Analysis",
author = "Li, Ruifan and
Chen, Hao and
Feng, Fangxiang and
Ma, Zhanyu and
Wang, Xiaojie and
Hovy, Eduard",
booktitle = "Proceedings of the 59th Annual Meeting of the Association for Computational Linguistics and the 11th International Joint Conference on Natural Language Processing (Volume 1: Long Papers)",
month = aug,
year = "2021",
address = "Online",
publisher = "Association for Computational Linguistics",
url = "https://aclanthology.org/2021.acl-long.494",
}