Neural network-based methods are the state of the art in negation scope resolution. However, they often use the unrealistic assumption that cue information is completely accurate. Even if this assumption holds, there remains a dependency on engineered features from state-of-the-art machine learning methods. The current study adopted a two-step negation resolving apporach to assess whether a Bidirectional Long Short-Term Memory-based method can be used for cue detection as well, and how inaccurate cue predictions would affect the scope resolution performance. Results suggest that this method is not suitable for negation detection. Scope resolution performance is most robust against inaccurate information for models with a recurrent layer only, compared to extensions with a Conditional Random Fields layer or a post-processing algorithm. We advocate for more research into the application of deep learning on negation detection and the effect of imperfect information on scope resolution.
NegBio: a high-performance tool for negation and uncertainty detection in radiology reports
National Center for Biotechnology Information, National Library of Medicine, National Institutes of Health（美国国立卫生研究院国家医学图书馆国家生物技术信息中心）；Department of Radiology and Imaging Sciences, Clinical Center, National Institutes of Health（美国国立卫生研究院临床中心放射与影像科学系）
Dimensional emotion recognition using visual and textual cues
Faculty of Engineering of University of Porto (FEUP)（波尔图大学工程学院）；INESC TEC（波尔图工程学院科技与高等研究中心）；Faculty of Science of University of Porto (FCUP)（波尔图大学理学院）；UCP-ESB（葡萄牙天主教大学商业经济学院）
