@inproceedings{huber-carenini-2020-sentiment,
title = "From Sentiment Annotations to Sentiment Prediction through Discourse Augmentation",
author = "Huber, Patrick and
Carenini, Giuseppe",
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.16/",
doi = "10.18653/v1/2020.coling-main.16",
pages = "185--197",
abstract = "Sentiment analysis, especially for long documents, plausibly requires methods capturing complex linguistics structures. To accommodate this, we propose a novel framework to exploit task-related discourse for the task of sentiment analysis. More specifically, we are combining the large-scale, sentiment-dependent MEGA-DT treebank with a novel neural architecture for sentiment prediction, based on a hybrid TreeLSTM hierarchical attention model. Experiments show that our framework using sentiment-related discourse augmentations for sentiment prediction enhances the overall performance for long documents, even beyond previous approaches using well-established discourse parsers trained on human annotated data. We show that a simple ensemble approach can further enhance performance by selectively using discourse, depending on the document length."
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%0 Conference Proceedings
%T From Sentiment Annotations to Sentiment Prediction through Discourse Augmentation
%A Huber, Patrick
%A Carenini, Giuseppe
%Y Scott, Donia
%Y Bel, Nuria
%Y Zong, Chengqing
%S Proceedings of the 28th International Conference on Computational Linguistics
%D 2020
%8 December
%I International Committee on Computational Linguistics
%C Barcelona, Spain (Online)
%F huber-carenini-2020-sentiment
%X Sentiment analysis, especially for long documents, plausibly requires methods capturing complex linguistics structures. To accommodate this, we propose a novel framework to exploit task-related discourse for the task of sentiment analysis. More specifically, we are combining the large-scale, sentiment-dependent MEGA-DT treebank with a novel neural architecture for sentiment prediction, based on a hybrid TreeLSTM hierarchical attention model. Experiments show that our framework using sentiment-related discourse augmentations for sentiment prediction enhances the overall performance for long documents, even beyond previous approaches using well-established discourse parsers trained on human annotated data. We show that a simple ensemble approach can further enhance performance by selectively using discourse, depending on the document length.
%R 10.18653/v1/2020.coling-main.16
%U https://aclanthology.org/2020.coling-main.16/
%U https://doi.org/10.18653/v1/2020.coling-main.16
%P 185-197
Markdown (Informal)
[From Sentiment Annotations to Sentiment Prediction through Discourse Augmentation](https://aclanthology.org/2020.coling-main.16/) (Huber & Carenini, COLING 2020)
ACL