GTS

Grid Tagging Scheme for Aspect-oriented Fine-grained Opinion Extraction. Zhen Wu, Chengcan Ying, Fei Zhao, Zhifang Fan, Xinyu Dai, Rui Xia. In Findings of EMNLP, 2020.

Data

[Data] [Pre-trained Model]

Data format descriptions are here.

Requirements

See requirement.txt or Pipfile for details

• pytorch==1.7.1
• transformers==3.4.0
• python=3.6

Usage

• Training

For example, you can use the folowing command to fine-tune Bert on the OPE task (the pre-trained Bert model is saved in the folder "pretrained/"):

python main.py --task pair --mode train --dataset res14

The best model will be saved in the folder "savemodel/".

• Testing

For example, you can use the folowing command to test Bert on the OPE task:

python main.py --task pair --mode test --dataset res14

Note: In our pre-experiments, a smaller batch size and learning rate can achieve better performance on certain datasets, while we use a general setting in our paper to save time instead of adopting grid search.

Citation

If you used the datasets or code, please cite our paper:

@inproceedings{wu-etal-2020-grid,
    title = "Grid Tagging Scheme for Aspect-oriented Fine-grained Opinion Extraction",
    author = "Wu, Zhen  and
      Ying, Chengcan  and
      Zhao, Fei  and
      Fan, Zhifang  and
      Dai, Xinyu  and
      Xia, Rui",
    booktitle = "Findings of the Association for Computational Linguistics: EMNLP 2020",
    month = nov,
    year = "2020",
    address = "Online",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/2020.findings-emnlp.234",
    doi = "10.18653/v1/2020.findings-emnlp.234",
    pages = "2576--2585",
}

Reference

[1]. Zhen Wu, Chengcan Ying, Fei Zhao, Zhifang Fan, Xinyu Dai, Rui Xia. Grid Tagging Scheme for Aspect-oriented Fine-grained Opinion Extraction. In Findings of EMNLP, 2020.

[2]. Zhifang Fan, Zhen Wu, Xin-Yu Dai, Shujian Huang, Jiajun Chen. Target-oriented Opinion Words Extraction with Target-fused Neural Sequence Labeling. In Proceedings of NAACL, 2019.