Code and datasets of our paper "AMR-based Network for Aspect-based Sentiment Analysis" accepted by ACL 2023.
- torch>=1.13.1
- scikit-learn==0.23.2
- transformers==3.2.0
- nltk==3.5
- einops==0.4.1
To install requirements, run pip install -r requirements.txt.
To train and evaluate the APARN model, run:
./APARN/run.sh
If having trouble downloading *.pt with git-lfs, you can use the following link instead: Tsinghua Cloud
The code and datasets in this repository are based on DualGCN and Alphafold2-Pytorch.
If you find this work useful, please cite as following.
@inproceedings{ma-etal-2023-amr,
title = "{AMR}-based Network for Aspect-based Sentiment Analysis",
author = "Ma, Fukun and
Hu, Xuming and
Liu, Aiwei and
Yang, Yawen and
Li, Shuang and
Yu, Philip S. and
Wen, Lijie",
editor = "Rogers, Anna and
Boyd-Graber, Jordan and
Okazaki, Naoaki",
booktitle = "Proceedings of the 61st Annual Meeting of the Association for Computational Linguistics (Volume 1: Long Papers)",
month = jul,
year = "2023",
address = "Toronto, Canada",
publisher = "Association for Computational Linguistics",
url = "https://aclanthology.org/2023.acl-long.19",
doi = "10.18653/v1/2023.acl-long.19",
pages = "322--337",
abstract = "Aspect-based sentiment analysis (ABSA) is a fine-grained sentiment classification task. Many recent works have used dependency trees to extract the relation between aspects and contexts and have achieved significant improvements. However, further improvement is limited due to the potential mismatch between the dependency tree as a syntactic structure and the sentiment classification as a semantic task. To alleviate this gap, we replace the syntactic dependency tree with the semantic structure named Abstract Meaning Representation (AMR) and propose a model called AMR-based Path Aggregation Relational Network (APARN) to take full advantage of semantic structures. In particular, we design the path aggregator and the relation-enhanced self-attention mechanism that complement each other. The path aggregator extracts semantic features from AMRs under the guidance of sentence information, while the relation-enhanced self-attention mechanism in turn improves sentence features with refined semantic information. Experimental results on four public datasets demonstrate 1.13{\%} average F1 improvement of APARN in ABSA when compared with state-of-the-art baselines.",
}