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https://ah.lib.nccu.edu.tw/handle/140.119/135528
題名: | Numeracy-600K: Learning Numeracy for Detecting Exaggerated Information in Market Comments | 作者: | 黃瀚萱 Huang, Hen-Hsen Chen, Chung-Chi Takamura, Hiroya Chen, Hsin-Hsi |
貢獻者: | 資科系 | 日期: | Jul-2019 | 上傳時間: | 4-Jun-2021 | 摘要: | In this paper, we attempt to answer the question of whether neural network models can learn numeracy, which is the ability to predict the magnitude of a numeral at some specific position in a text description. A large benchmark dataset, called Numeracy-600K, is provided for the novel task. We explore several neural network models including CNN, GRU, BiGRU, CRNN, CNN-capsule, GRU-capsule, and BiGRU-capsule in the experiments. The results show that the BiGRU model gets the best micro-averaged F1 score of 80.16%, and the GRU-capsule model gets the best macroaveraged F1 score of 64.71%. Besides discussing the challenges through comprehensive experiments, we also present an important application scenario, i.e., detecting exaggerated information, for the task. | 關聯: | Proceedings of the 57th Annual Meeting of the Association for Computational Linguistics, ACL, pp.6307–6313 | 資料類型: | conference | DOI: | http://dx.doi.org/10.18653/v1/P19-1635 |
Appears in Collections: | 會議論文 |
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