allenfeizz / dualgcn-absa

Dual Graph Convolutional Networks for Aspect-based Sentiment Analysis

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

1. Download and unzip GloVe vectors(glove.840B.300d.zip) from https://nlp.stanford.edu/projects/glove/ and put it into DualGCN/glove directory.

2. Prepare vocabulary with:

   sh DualGCN/build_vocab.sh

3. 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",
}