@inproceedings{xiang-etal-2020-ciron,
title = "{C}iron: a New Benchmark Dataset for {C}hinese Irony Detection",
author = "Xiang, Rong and
Gao, Xuefeng and
Long, Yunfei and
Li, Anran and
Chersoni, Emmanuele and
Lu, Qin and
Huang, Chu-Ren",
booktitle = "Proceedings of the 12th Language Resources and Evaluation Conference",
month = may,
year = "2020",
address = "Marseille, France",
publisher = "European Language Resources Association",
url = "https://aclanthology.org/2020.lrec-1.701",
pages = "5714--5720",
abstract = "Automatic Chinese irony detection is a challenging task, and it has a strong impact on linguistic research. However, Chinese irony detection often lacks labeled benchmark datasets. In this paper, we introduce Ciron, the first Chinese benchmark dataset available for irony detection for machine learning models. Ciron includes more than 8.7K posts, collected from Weibo, a micro blogging platform. Most importantly, Ciron is collected with no pre-conditions to ensure a much wider coverage. Evaluation on seven different machine learning classifiers proves the usefulness of Ciron as an important resource for Chinese irony detection.",
language = "English",
ISBN = "979-10-95546-34-4",
}
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<abstract>Automatic Chinese irony detection is a challenging task, and it has a strong impact on linguistic research. However, Chinese irony detection often lacks labeled benchmark datasets. In this paper, we introduce Ciron, the first Chinese benchmark dataset available for irony detection for machine learning models. Ciron includes more than 8.7K posts, collected from Weibo, a micro blogging platform. Most importantly, Ciron is collected with no pre-conditions to ensure a much wider coverage. Evaluation on seven different machine learning classifiers proves the usefulness of Ciron as an important resource for Chinese irony detection.</abstract>
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%0 Conference Proceedings
%T Ciron: a New Benchmark Dataset for Chinese Irony Detection
%A Xiang, Rong
%A Gao, Xuefeng
%A Long, Yunfei
%A Li, Anran
%A Chersoni, Emmanuele
%A Lu, Qin
%A Huang, Chu-Ren
%S Proceedings of the 12th Language Resources and Evaluation Conference
%D 2020
%8 may
%I European Language Resources Association
%C Marseille, France
%@ 979-10-95546-34-4
%G English
%F xiang-etal-2020-ciron
%X Automatic Chinese irony detection is a challenging task, and it has a strong impact on linguistic research. However, Chinese irony detection often lacks labeled benchmark datasets. In this paper, we introduce Ciron, the first Chinese benchmark dataset available for irony detection for machine learning models. Ciron includes more than 8.7K posts, collected from Weibo, a micro blogging platform. Most importantly, Ciron is collected with no pre-conditions to ensure a much wider coverage. Evaluation on seven different machine learning classifiers proves the usefulness of Ciron as an important resource for Chinese irony detection.
%U https://aclanthology.org/2020.lrec-1.701
%P 5714-5720
Markdown (Informal)
[Ciron: a New Benchmark Dataset for Chinese Irony Detection](https://aclanthology.org/2020.lrec-1.701) (Xiang et al., LREC 2020)
ACL
- Rong Xiang, Xuefeng Gao, Yunfei Long, Anran Li, Emmanuele Chersoni, Qin Lu, and Chu-Ren Huang. 2020. Ciron: a New Benchmark Dataset for Chinese Irony Detection. In Proceedings of the 12th Language Resources and Evaluation Conference, pages 5714–5720, Marseille, France. European Language Resources Association.