@inproceedings{zhang-etal-2016-tweet,
title = "Tweet Sarcasm Detection Using Deep Neural Network",
author = "Zhang, Meishan and
Zhang, Yue and
Fu, Guohong",
editor = "Matsumoto, Yuji and
Prasad, Rashmi",
booktitle = "Proceedings of {COLING} 2016, the 26th International Conference on Computational Linguistics: Technical Papers",
month = dec,
year = "2016",
address = "Osaka, Japan",
publisher = "The COLING 2016 Organizing Committee",
url = "https://aclanthology.org/C16-1231",
pages = "2449--2460",
abstract = "Sarcasm detection has been modeled as a binary document classification task, with rich features being defined manually over input documents. Traditional models employ discrete manual features to address the task, with much research effect being devoted to the design of effective feature templates. We investigate the use of neural network for tweet sarcasm detection, and compare the effects of the continuous automatic features with discrete manual features. In particular, we use a bi-directional gated recurrent neural network to capture syntactic and semantic information over tweets locally, and a pooling neural network to extract contextual features automatically from history tweets. Results show that neural features give improved accuracies for sarcasm detection, with different error distributions compared with discrete manual features.",
}
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%0 Conference Proceedings
%T Tweet Sarcasm Detection Using Deep Neural Network
%A Zhang, Meishan
%A Zhang, Yue
%A Fu, Guohong
%Y Matsumoto, Yuji
%Y Prasad, Rashmi
%S Proceedings of COLING 2016, the 26th International Conference on Computational Linguistics: Technical Papers
%D 2016
%8 December
%I The COLING 2016 Organizing Committee
%C Osaka, Japan
%F zhang-etal-2016-tweet
%X Sarcasm detection has been modeled as a binary document classification task, with rich features being defined manually over input documents. Traditional models employ discrete manual features to address the task, with much research effect being devoted to the design of effective feature templates. We investigate the use of neural network for tweet sarcasm detection, and compare the effects of the continuous automatic features with discrete manual features. In particular, we use a bi-directional gated recurrent neural network to capture syntactic and semantic information over tweets locally, and a pooling neural network to extract contextual features automatically from history tweets. Results show that neural features give improved accuracies for sarcasm detection, with different error distributions compared with discrete manual features.
%U https://aclanthology.org/C16-1231
%P 2449-2460
Markdown (Informal)
[Tweet Sarcasm Detection Using Deep Neural Network](https://aclanthology.org/C16-1231) (Zhang et al., COLING 2016)
ACL
- Meishan Zhang, Yue Zhang, and Guohong Fu. 2016. Tweet Sarcasm Detection Using Deep Neural Network. In Proceedings of COLING 2016, the 26th International Conference on Computational Linguistics: Technical Papers, pages 2449–2460, Osaka, Japan. The COLING 2016 Organizing Committee.