@inproceedings{hou-etal-2021-selective,
title = "Selective Attention Based Graph Convolutional Networks for Aspect-Level Sentiment Classification",
author = "Hou, Xiaochen and
Huang, Jing and
Wang, Guangtao and
Qi, Peng and
He, Xiaodong and
Zhou, Bowen",
editor = "Panchenko, Alexander and
Malliaros, Fragkiskos D. and
Logacheva, Varvara and
Jana, Abhik and
Ustalov, Dmitry and
Jansen, Peter",
booktitle = "Proceedings of the Fifteenth Workshop on Graph-Based Methods for Natural Language Processing (TextGraphs-15)",
month = jun,
year = "2021",
address = "Mexico City, Mexico",
publisher = "Association for Computational Linguistics",
url = "https://aclanthology.org/2021.textgraphs-1.8/",
doi = "10.18653/v1/2021.textgraphs-1.8",
pages = "83--93",
abstract = "Recent work on aspect-level sentiment classification has employed Graph Convolutional Networks (GCN) over dependency trees to learn interactions between aspect terms and opinion words. In some cases, the corresponding opinion words for an aspect term cannot be reached within two hops on dependency trees, which requires more GCN layers to model. However, GCNs often achieve the best performance with two layers, and deeper GCNs do not bring any additional gain. Therefore, we design a novel selective attention based GCN model. On one hand, the proposed model enables the direct interaction between aspect terms and context words via the self-attention operation without the distance limitation on dependency trees. On the other hand, a top-k selection procedure is designed to locate opinion words by selecting k context words with the highest attention scores. We conduct experiments on several commonly used benchmark datasets and the results show that our proposed SA-GCN outperforms strong baseline models."
}
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<abstract>Recent work on aspect-level sentiment classification has employed Graph Convolutional Networks (GCN) over dependency trees to learn interactions between aspect terms and opinion words. In some cases, the corresponding opinion words for an aspect term cannot be reached within two hops on dependency trees, which requires more GCN layers to model. However, GCNs often achieve the best performance with two layers, and deeper GCNs do not bring any additional gain. Therefore, we design a novel selective attention based GCN model. On one hand, the proposed model enables the direct interaction between aspect terms and context words via the self-attention operation without the distance limitation on dependency trees. On the other hand, a top-k selection procedure is designed to locate opinion words by selecting k context words with the highest attention scores. We conduct experiments on several commonly used benchmark datasets and the results show that our proposed SA-GCN outperforms strong baseline models.</abstract>
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%0 Conference Proceedings
%T Selective Attention Based Graph Convolutional Networks for Aspect-Level Sentiment Classification
%A Hou, Xiaochen
%A Huang, Jing
%A Wang, Guangtao
%A Qi, Peng
%A He, Xiaodong
%A Zhou, Bowen
%Y Panchenko, Alexander
%Y Malliaros, Fragkiskos D.
%Y Logacheva, Varvara
%Y Jana, Abhik
%Y Ustalov, Dmitry
%Y Jansen, Peter
%S Proceedings of the Fifteenth Workshop on Graph-Based Methods for Natural Language Processing (TextGraphs-15)
%D 2021
%8 June
%I Association for Computational Linguistics
%C Mexico City, Mexico
%F hou-etal-2021-selective
%X Recent work on aspect-level sentiment classification has employed Graph Convolutional Networks (GCN) over dependency trees to learn interactions between aspect terms and opinion words. In some cases, the corresponding opinion words for an aspect term cannot be reached within two hops on dependency trees, which requires more GCN layers to model. However, GCNs often achieve the best performance with two layers, and deeper GCNs do not bring any additional gain. Therefore, we design a novel selective attention based GCN model. On one hand, the proposed model enables the direct interaction between aspect terms and context words via the self-attention operation without the distance limitation on dependency trees. On the other hand, a top-k selection procedure is designed to locate opinion words by selecting k context words with the highest attention scores. We conduct experiments on several commonly used benchmark datasets and the results show that our proposed SA-GCN outperforms strong baseline models.
%R 10.18653/v1/2021.textgraphs-1.8
%U https://aclanthology.org/2021.textgraphs-1.8/
%U https://doi.org/10.18653/v1/2021.textgraphs-1.8
%P 83-93
Markdown (Informal)
[Selective Attention Based Graph Convolutional Networks for Aspect-Level Sentiment Classification](https://aclanthology.org/2021.textgraphs-1.8/) (Hou et al., TextGraphs 2021)
ACL