@inproceedings{zhang-zhang-2019-tree,
title = "Tree Communication Models for Sentiment Analysis",
author = "Zhang, Yuan and
Zhang, Yue",
editor = "Korhonen, Anna and
Traum, David and
M{\`a}rquez, Llu{\'\i}s",
booktitle = "Proceedings of the 57th Annual Meeting of the Association for Computational Linguistics",
month = jul,
year = "2019",
address = "Florence, Italy",
publisher = "Association for Computational Linguistics",
url = "https://aclanthology.org/P19-1342",
doi = "10.18653/v1/P19-1342",
pages = "3518--3527",
abstract = "Tree-LSTMs have been used for tree-based sentiment analysis over Stanford Sentiment Treebank, which allows the sentiment signals over hierarchical phrase structures to be calculated simultaneously. However, traditional tree-LSTMs capture only the bottom-up dependencies between constituents. In this paper, we propose a tree communication model using graph convolutional neural network and graph recurrent neural network, which allows rich information exchange between phrases constituent tree. Experiments show that our model outperforms existing work on bidirectional tree-LSTMs in both accuracy and efficiency, providing more consistent predictions on phrase-level sentiments.",
}
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%0 Conference Proceedings
%T Tree Communication Models for Sentiment Analysis
%A Zhang, Yuan
%A Zhang, Yue
%Y Korhonen, Anna
%Y Traum, David
%Y Màrquez, Lluís
%S Proceedings of the 57th Annual Meeting of the Association for Computational Linguistics
%D 2019
%8 July
%I Association for Computational Linguistics
%C Florence, Italy
%F zhang-zhang-2019-tree
%X Tree-LSTMs have been used for tree-based sentiment analysis over Stanford Sentiment Treebank, which allows the sentiment signals over hierarchical phrase structures to be calculated simultaneously. However, traditional tree-LSTMs capture only the bottom-up dependencies between constituents. In this paper, we propose a tree communication model using graph convolutional neural network and graph recurrent neural network, which allows rich information exchange between phrases constituent tree. Experiments show that our model outperforms existing work on bidirectional tree-LSTMs in both accuracy and efficiency, providing more consistent predictions on phrase-level sentiments.
%R 10.18653/v1/P19-1342
%U https://aclanthology.org/P19-1342
%U https://doi.org/10.18653/v1/P19-1342
%P 3518-3527
Markdown (Informal)
[Tree Communication Models for Sentiment Analysis](https://aclanthology.org/P19-1342) (Zhang & Zhang, ACL 2019)
ACL
- Yuan Zhang and Yue Zhang. 2019. Tree Communication Models for Sentiment Analysis. In Proceedings of the 57th Annual Meeting of the Association for Computational Linguistics, pages 3518–3527, Florence, Italy. Association for Computational Linguistics.