https://github.com/cuhksz-nlp/DGSA

https://github.com/cuhksz-nlp/DGSA

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• Host: GitHub
• URL: https://github.com/cuhksz-nlp/DGSA
• Owner: cuhksz-nlp
• License: mit
• Created: 2020-07-12T05:41:17.000Z (over 4 years ago)
• Default Branch: master
• Last Pushed: 2022-12-23T04:25:15.000Z (almost 2 years ago)
• Last Synced: 2024-08-03T09:07:27.736Z (5 months ago)
• Language: Python
• Size: 65.4 KB
• Stars: 29
• Watchers: 2
• Forks: 8
• Open Issues: 3
• Metadata Files:
  • Readme: README.md
  • License: LICENSE

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README

        
# DGSA

This is the implementation of [Joint Aspect Extraction and Sentiment Analysis with Directional Graph Convolutional Networks](https://www.aclweb.org/anthology/2020.coling-main.24/) at COLING 2020.

You can e-mail Yuanhe Tian at `[email protected]`, if you have any questions.

**Visit our [homepage](https://github.com/synlp/.github) to find more our recent research and softwares for NLP (e.g., pre-trained LM, POS tagging, NER, sentiment analysis, relation extraction, datasets, etc.).**

## Upgrades of DGSA

We are improving our DGSA. For updates, please visit [HERE](https://github.com/synlp/DGSA).

## Citation

If you use or extend our work, please cite our paper at COLING 2020.

```

@inproceedings{chen-etal-2020-joint-aspect,

    title = "Joint Aspect Extraction and Sentiment Analysis with Directional Graph Convolutional Networks",

    author = "Chen, Guimin  and Tian, Yuanhe  and Song, Yan",

    booktitle = "Proceedings of the 28th International Conference on Computational Linguistics",

    month = dec,

    year = "2020",

    address = "Barcelona, Spain (Online)",

    pages = "272--279",

}

```

## Requirements

Our code works with the following environment.

* `python=3.7`

* `pytorch=1.3`

## Dataset

To obtain the data, you can go to [`data`](./data) directory for details.

## Downloading BERT and DGSA

In our paper, we use BERT ([paper](https://www.aclweb.org/anthology/N19-1423/)) as the encoder.

For BERT, please download pre-trained BERT-Base and BERT-Large English from [Google](https://github.com/google-research/bert) or from [HuggingFace](https://s3.amazonaws.com/models.huggingface.co/bert/bert-base-chinese.tar.gz). If you download it from Google, you need to convert the model from TensorFlow version to PyTorch version.

For DGSA, you can download the models we trained in our experiments from [Google Drive](https://drive.google.com/drive/folders/1U78sBVGn5Uj0EP-nSl46LFgS8RgbJxJ9?usp=sharing) or [Baidu Net Disk](https://pan.baidu.com/s/1eaY8KBXj3z_gfST7MpNqMw) (passcode: u6gp).

## Run on Sample Data

Run `run_sample.sh` to train a model on the small sample data under the `sample_data` directory.

## Training and Testing

You can find the command lines to train and test models in `run_train.sh` and `run_test.sh`, respectively.

Here are some important parameters:

* `--do_train`: train the model.

* `--do_eval`: test the model.

## To-do List

* Regular maintenance.

You can leave comments in the `Issues` section, if you want us to implement any functions.