IARM

This repo contains the source code of the paper —

IARM: Inter-Aspect Relation Modeling with Memory Networks in Aspect-Based Sentiment Analysis.
Navonil Majumder, Soujanya Poria, Alexander Gelbukh, Md. Shad Akhtar, Erik Cambria, Asif Ekbal. EMNLP 2018

This method attempts to model the relationship among the different aspect-terms in a sentence using memory networks to enable better sentiment classification of the aspects.

Requirements

• Python 2.7
• PyTorch 0.3
• Keras 1.0

Execution

Execute the file ABSA-emb-gpu-final-newarch3.py for training and testing on SemEval 2014 ABSA dataset.
The following are the command-line arguments:

• --no-cuda: GPU is not used
• --lr: set learning rate
• --l2: set L2-norm weight
• --batch-size: set batch size
• --epochs: set number of epochs
• --hops: set number hops of memory network
• --hidden-size: set hidden representation size
• --output-size: set output representation size
• --dropout-p: set dropout probability
• --dropout-lstm: set recurrent dropout probability
• --dataset: set which dataset to use - Restaurants or Laptop

Example:

python ABSA-emb-gpu-final-newarch3.py --lr 0.001 --l2 0.0001 --dataset Laptop --hops 3 --epochs 30 --hidden-size 400 --output-size 300 --dropout-p 0.1 --dropout-lstm 0.2

Citation

If you find this code useful please cite the following in your work:

@InProceedings{D18-1377,
  author =     "Majumder, Navonil
        and Poria, Soujanya
        and Gelbukh, Alexander
        and Akhtar, Md Shad
        and Cambria, Erik
        and Ekbal, Asif",
  title =     "IARM: Inter-Aspect Relation Modeling with Memory Networks in Aspect-Based Sentiment Analysis",
  booktitle =     "Proceedings of the 2018 Conference on Empirical Methods in Natural Language Processing",
  year =     "2018",
  publisher =     "Association for Computational Linguistics",
  pages =     "3402--3411",
  location =     "Brussels, Belgium",
  url =     "http://aclweb.org/anthology/D18-1377"
}

Credits

Codes were written by Soujanya Poria and Navonil Majumder