An Aspect-Centralized Graph Convolutional Network for Aspect-based Sentiment Classification

ACGCN - Aspect Centralized Graph Convolutional Network

• Code for NLPCC 2021 accepted paper titled "An Aspect-Centralized Graph Convolutional Network for Aspect-based Sentiment Classification"
• Weixiang Zhao, Yanyan Zhao, Xin Lu and Bing Qin.

Requirements

• Python 3.7
• PyTorch 1.6.0
• Supar 1.0.0

Usage

• Install SpaCy package and language models with

pip install spacy

and

python -m spacy download en

• Install Biaffine parser with

pip install -U supar

• Generate Aspect-Centralized Graph with

python generate_acg.py

• Download pretrained GloVe embeddings with this link and extract glove.840B.300d.txt into glove/.

Train

• optional arguments could be found in train.py
• For dataset Lap14

python train.py --model_name acgcn --embed_type glove --layernorm True --highway True --batch_size 16 --dataset lap14

python train.py --model_name acgcn_bert --embed_type bert --hidden_dim 768 --learning_rate 5e-5 --dataset lap14

• For dataset Rest14

python train.py --model_name acgcn --embed_type glove --layernorm True --highway True --batch_size 16 --dataset rest14

python train.py --model_name acgcn_bert --embed_type bert --hidden_dim 768 --learning_rate 5e-5 --dataset rest14

• For dataset Rest15

python train.py --model_name acgcn --embed_type glove --dataset rest15

python train.py --learning_rate 5e-5 --model_name asgcn_bert --embed_type bert --hidden_dim 768 --dataset rest15

• For dataset Rest16

python train.py --model_name acgcn --embed_type glove --highway True --dataset rest16

python train.py --learning_rate 5e-5 --model_name asgcn_bert --embed_type bert --hidden_dim 768 --dataset rest16

• For dataset Twitter

python train.py --model_name acgcn --embed_type glove --layernorm True --dataset twitter

python train.py --learning_rate 5e-5 --model_name asgcn_bert --embed_type bert --hidden_dim 768 --layernorm True --dataset twitter

Model

An overview of proposed ACGCN model structure is given below

Credits

• The code of this repository partly relies on ASGCN and I would like to show my sincere gratitude to authors of it.