Syntax-Aware Graph Attention Network for Aspect-Level Sentiment Classification
Lianzhe Huang, Xin Sun, Sujian Li, Linhao Zhang, Houfeng Wang
Abstract
Aspect-level sentiment classification aims to distinguish the sentiment polarities over aspect terms in a sentence. Existing approaches mostly focus on modeling the relationship between the given aspect words and their contexts with attention, and ignore the use of more elaborate knowledge implicit in the context. In this paper, we exploit syntactic awareness to the model by the graph attention network on the dependency tree structure and external pre-training knowledge by BERT language model, which helps to model the interaction between the context and aspect words better. And the subwords of BERT are integrated into the dependency tree graphs, which can obtain more accurate representations of words by graph attention. Experiments demonstrate the effectiveness of our model.
BibTeX
@inproceedings{huang-etal-2020-syntax,
title = "Syntax-Aware Graph Attention Network for Aspect-Level Sentiment Classification",
author = "Huang, Lianzhe and
Sun, Xin and
Li, Sujian and
Zhang, Linhao and
Wang, Houfeng",
editor = "Scott, Donia and
Bel, Nuria and
Zong, Chengqing",
booktitle = "Proceedings of the 28th International Conference on Computational Linguistics",
month = dec,
year = "2020",
address = "Barcelona, Spain (Online)",
publisher = "International Committee on Computational Linguistics",
url = "https://aclanthology.org/2020.coling-main.69/",
doi = "10.18653/v1/2020.coling-main.69",
pages = "799--810"
}