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The code of our paper "Improving Aspect Sentiment Triplet Extraction with Perturbed Masking and Edge-Enhanced Sentiment Graph Attention Network" accepted by IJCNN 2023.

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ES-GAT

The code of our paper "Improving Aspect Sentiment Triplet Extraction with Perturbed Masking and Edge-Enhanced Sentiment Graph Attention Network" accepted by IJCNN 2023.

Requirements

  • python==3.7.6

  • torch==1.4.0

  • transformers==3.4.0

  • argparse==1.1

Training

To train the model, run:

cd ./code
sh run.sh

or

python main.py --mode train --dataset res14 --batch_size 16 --epochs 100 --model_dir savemodel/ --seed 1000 --pooling avg --prefix ../data/D2/

Acknowledge

The basic code framework is based on "https://github.com/CCChenhao997/EMCGCN-ASTE", thanks for the contribution of its open source code.

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The code of our paper "Improving Aspect Sentiment Triplet Extraction with Perturbed Masking and Edge-Enhanced Sentiment Graph Attention Network" accepted by IJCNN 2023.

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