ASGCN

ASGCN - Aspect-Specific Graph Convolutional Network

• Code and preprocessed dataset for EMNLP 2019 paper titled "Aspect-based Sentiment Classification with Aspect-specific Graph Convolutional Networks"
• Chen Zhang, Qiuchi Li and Dawei Song.

Requirements

• Python 3.6
• PyTorch 1.0.0
• SpaCy 2.0.18
• numpy 1.15.4

Usage

• Install SpaCy package and language models with

pip install spacy

and

python -m spacy download en

• Download pretrained GloVe embeddings with this link and extract glove.840B.300d.txt into glove/.
• Train with command, optional arguments could be found in train.py

python train.py --model_name asgcn --dataset rest14 --save True

• Infer with infer.py

Model

we propose to build a Graph Convolutional Network (GCN) over the dependency tree of a sentence to exploit syntactical information and word dependencies. Based on it, a novel aspectspecific sentiment classification framework is raised.

An overview of our proposed model is given below

Citation

If you use the code in your paper, please kindly star this repo and cite our paper

@inproceedings{Zhang,
 author = {Zhang, Chen and Li, Qiuchi and Song, Dawei},
 title = {Aspect-based Sentiment Classification with Aspect-specific Graph Convolutional Networks},
 series = {EMNLP'19},
 year = {2019},
 publisher = {ACL},
}

Credits

• Code of this repo heavily relies on ABSA-PyTorch, in which I am one of the contributors.
• For any issues or suggestions about this work, don't hesitate to create an issue or directly contact me via [email protected] !