@inproceedings{huang-carley-2019-syntax,
title = "Syntax-Aware Aspect Level Sentiment Classification with Graph Attention Networks",
author = "Huang, Binxuan and
Carley, Kathleen",
editor = "Inui, Kentaro and
Jiang, Jing and
Ng, Vincent and
Wan, Xiaojun",
booktitle = "Proceedings of the 2019 Conference on Empirical Methods in Natural Language Processing and the 9th International Joint Conference on Natural Language Processing (EMNLP-IJCNLP)",
month = nov,
year = "2019",
address = "Hong Kong, China",
publisher = "Association for Computational Linguistics",
url = "https://aclanthology.org/D19-1549",
doi = "10.18653/v1/D19-1549",
pages = "5469--5477",
abstract = "Aspect level sentiment classification aims to identify the sentiment expressed towards an aspect given a context sentence. Previous neural network based methods largely ignore the syntax structure in one sentence. In this paper, we propose a novel target-dependent graph attention network (TD-GAT) for aspect level sentiment classification, which explicitly utilizes the dependency relationship among words. Using the dependency graph, it propagates sentiment features directly from the syntactic context of an aspect target. In our experiments, we show our method outperforms multiple baselines with GloVe embeddings. We also demonstrate that using BERT representations further substantially boosts the performance.",
}
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%0 Conference Proceedings
%T Syntax-Aware Aspect Level Sentiment Classification with Graph Attention Networks
%A Huang, Binxuan
%A Carley, Kathleen
%Y Inui, Kentaro
%Y Jiang, Jing
%Y Ng, Vincent
%Y Wan, Xiaojun
%S Proceedings of the 2019 Conference on Empirical Methods in Natural Language Processing and the 9th International Joint Conference on Natural Language Processing (EMNLP-IJCNLP)
%D 2019
%8 November
%I Association for Computational Linguistics
%C Hong Kong, China
%F huang-carley-2019-syntax
%X Aspect level sentiment classification aims to identify the sentiment expressed towards an aspect given a context sentence. Previous neural network based methods largely ignore the syntax structure in one sentence. In this paper, we propose a novel target-dependent graph attention network (TD-GAT) for aspect level sentiment classification, which explicitly utilizes the dependency relationship among words. Using the dependency graph, it propagates sentiment features directly from the syntactic context of an aspect target. In our experiments, we show our method outperforms multiple baselines with GloVe embeddings. We also demonstrate that using BERT representations further substantially boosts the performance.
%R 10.18653/v1/D19-1549
%U https://aclanthology.org/D19-1549
%U https://doi.org/10.18653/v1/D19-1549
%P 5469-5477
Markdown (Informal)
[Syntax-Aware Aspect Level Sentiment Classification with Graph Attention Networks](https://aclanthology.org/D19-1549) (Huang & Carley, EMNLP-IJCNLP 2019)
ACL