Word Sense Disambiguation with Knowledge-Enhanced and Local Self-Attention-based Extractive Sense Comprehension

Code for the COLING2022 paper "Word Sense Disambiguation with Knowledge-Enhanced and Local Self-Attention-based Extractive Sense Comprehension" [Paper]

Environment

• black=21.5b2
• nlp=0.4.0
• nltk=3.4.5
• numpy=1.21.2
• pandas=1.3.5
• python=3.7.12
• pytorch=1.8.0
• pytorch-lightning=0.9.0
• tqdm=4.64.0
• transformers=3.2.0
• wandb=0.12.12

Data Preparation

We follow the unified evaluation framework (raganato framework) for WSD. You can download the dataset here and unpack it in the data folder.

Train the WSD Model on the Dataset

If you want to train your own model you just have to run the following command in the KELESC folder:

python model/train.py

Evaluation

If you want to evaluate the model on a dataset, just run the following command in the KELESC folder:

python model/predict.py --ckpt <kelesc_checkpoint.ckpt> --dataset-paths data/WSD_Evaluation_Framework/Evaluation_Datasets/semeval2007/semeval2007.data.xml 

The --dataset-paths can be modified. For example, change /semeval2007/semeval2007.data.xml to /semeval2013/semeval2013.data.xml. The predictions will be saved in the folder predictions with the name <dataset_name>_predictions.txt.

Citation

Please cite our paper if you find it helpful.

@inproceedings{,
    title = "Word Sense Disambiguation with Knowledge-Enhanced and Local Self-Attention-based Extractive Sense Comprehension",
    author = "Guobiao Zhang and 
      Wenpeng Lu and
      Xueping Peng and
      Shoujin Wang and
      Baoshuo Kan and
      Rui Yu",
    booktitle = "Proceedings of the 29th International Conference on Computational Linguistics",
    year = "2022",
}

Acknowledgement

Parts of the code are modified from ESC. We appreciate the authors for making ESC open-sourced.