Xiang, Rong and Gao, Xuefeng and Long, Yunfei and Li, Anran and Chersoni, Emmanuele and Lu, Qin and Huang, Chu-Ren (2020) Ciron: a New Benchmark Dataset for Chinese Irony Detection. In: 12th Language Resources and Evaluation Conference, 2020-05-11 - 2020-05-16, Marseille, France.
Xiang, Rong and Gao, Xuefeng and Long, Yunfei and Li, Anran and Chersoni, Emmanuele and Lu, Qin and Huang, Chu-Ren (2020) Ciron: a New Benchmark Dataset for Chinese Irony Detection. In: 12th Language Resources and Evaluation Conference, 2020-05-11 - 2020-05-16, Marseille, France.
Xiang, Rong and Gao, Xuefeng and Long, Yunfei and Li, Anran and Chersoni, Emmanuele and Lu, Qin and Huang, Chu-Ren (2020) Ciron: a New Benchmark Dataset for Chinese Irony Detection. In: 12th Language Resources and Evaluation Conference, 2020-05-11 - 2020-05-16, Marseille, France.
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.
Item Type: | Conference or Workshop Item (Paper) |
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Additional Information: | Published proceedings: Proceedings of the 12th Language Resources and Evaluation Conference |
Uncontrolled Keywords: | Irony detection; Chinese benchmark dataset; social media text; text processing |
Divisions: | Faculty of Science and Health Faculty of Science and Health > Computer Science and Electronic Engineering, School of |
SWORD Depositor: | Unnamed user with email elements@essex.ac.uk |
Depositing User: | Unnamed user with email elements@essex.ac.uk |
Date Deposited: | 03 Dec 2020 10:38 |
Last Modified: | 30 Oct 2024 19:20 |
URI: | http://repository.essex.ac.uk/id/eprint/29276 |
Available files
Filename: 2020.lrec-1.701.pdf
Licence: Creative Commons: Attribution 3.0