Named Entity Recognition (NER) is a low-level task often used as a foundation for solving higher level NLP problems. In the context of character detection in novels, NER false negatives can be an issue as they possibly imply missing certain characters or relationships completely. In this article, we demonstrate that applying a straightforward data augmentation technique allows training a model achieving higher recall, at the cost of a certain amount of precision regarding ambiguous entities. We show that this decrease in precision can be mitigated by giving the model more local context, which resolves some of the ambiguities.
TiSpell: A Semi-Masked Methodology for Tibetan Spelling Correction covering Multi-Level Error with Data Augmentation
School of Information and Software Engineering, University of Electronic Science and Technology of China（电子科技大学信息与软件工程学院）；School of Information Science and Technology, Tibet University（西藏大学信息科学与技术学院）；Department of Ophthalmology, University of Texas Southwestern Medical Center（德克萨斯西南医学中心眼科部门）
Elderly-Contextual Data Augmentation via Speech Synthesis for Elderly ASR
Department of Computer Science and Artificial Intelligence, Dongguk University（计算机科学与人工智能系，东国大学）；Department of Biostatistics, Harvard University（生物统计学系，哈佛大学）
Tokenization vs. Augmentation: A Systematic Study of Writer Variance in IMU-Based Online Handwriting Recognition
Friedrich-Alexander-Universität Erlangen-Nürnberg（埃尔朗根-纽伦堡弗里德里希-亚历山大大学）；STABILO International GmbH（STABILO国际有限公司）；Ludwig-Maximilians-Universität München（慕尼黑路德维希-马克西米利安大学）；Munich Center for Machine Learning（慕尼黑机器学习中心）；Helmholtz Zentrum München - German Research Center for Environmental Health（慕尼黑海德堡中心 - 德国环境健康研究中心）
Enhancing Effectiveness and Robustness in a Low-Resource Regime via Decision-Boundary-aware Data Augmentation
Chung-Ang University（中央大学）；Graduate School of Advanced Imaging Sciences, Multimedia and Film, Chung-Ang University（中央大学高级影像科学、多媒体与电影研究生院）；Department of Artificial Intelligence, Chung-Ang University（中央大学人工智能系）
