{"about":{"site":"https://codewithpapers.app","non_affiliation":"Code with Papers and Syntology are not affiliated with, endorsed by, or sponsored by Papers with Code, Meta, or the pwc-archive mirror.","licence":"CC BY-SA 4.0","licence_url":"https://creativecommons.org/licenses/by-sa/4.0/legalcode","attribution":"https://codewithpapers.app/attribution","modified":"archive material modified by Syntology; see the attribution page"},"url":"/paper/numeracy-600k-learning-numeracy-for-detecting","title":"Numeracy-600K: Learning Numeracy for Detecting Exaggerated Information in Market Comments","arxiv_id":null,"date":"2019-07-01","proceeding":"ACL 2019 7","authors":["Chung-Chi Chen","Hen-Hsen Huang","Hiroya Takamura","Hsin-Hsi Chen"],"abstract":"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 macro-averaged 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.","url_abs":"https://aclanthology.org/P19-1635","url_pdf":"https://aclanthology.org/P19-1635.pdf","source":{"archive":"pwc-archive (Hugging Face), CC BY-SA 4.0","snapshot":"2025-07-28","licence_url":"https://creativecommons.org/licenses/by-sa/4.0/legalcode","row_kind":"abstracts"},"code_links":[{"paper_slug":"numeracy-600k-learning-numeracy-for-detecting","repo_url":"https://github.com/aistairc/Numeracy-600K","is_official":1,"mentioned_in_paper":1,"mentioned_in_github":0,"framework":"none","reach":null}],"tasks":[{"task_slug":null,"task_name":"Position"}],"methods":[{"method_slug":"bigru","method_name":"BiGRU"},{"method_slug":"gru","method_name":"GRU"}],"datasets_introduced":[],"methods_introduced":[],"results":[],"syntology":{"atlas_url":null,"mcp":null,"developers":"https://syntology.ai/developers"},"arxiv_metadata":null,"syntology_extracted_results":null}