Du Jiahua, Michalska Sandra, Subramani Sudha, Wang Hua, Zhang Yanchun
Institute of Sustainable Industries & Liveable Cities, Victoria University, Melbourne, VIC Australia.
Health Inf Sci Syst. 2019 Oct 12;7(1):21. doi: 10.1007/s13755-019-0084-2. eCollection 2019 Dec.
The paper aims to leverage the highly unstructured user-generated content in the context of pollen allergy surveillance using neural networks with character embeddings and the attention mechanism. Currently, there is no accurate representation of hay fever prevalence, particularly in real-time scenarios. Social media serves as an alternative to extract knowledge about the condition, which is valuable for allergy sufferers, general practitioners, and policy makers. Despite tremendous potential offered, conventional natural language processing methods prove limited when exposed to the challenging nature of user-generated content. As a result, the detection of hay fever instances among the number of false positives, as well as the correct identification of non-technical expressions as pollen allergy symptoms poses a major problem. We propose a deep architecture enhanced with character embeddings and neural attention to improve the performance of hay fever-related content classification from Twitter data. Improvement in prediction is achieved due to the character-level semantics introduced, which effectively addresses the out-of-vocabulary problem in our dataset where the rate is approximately 9%. Overall, the study is a step forward towards improved real-time pollen allergy surveillance from social media with state-of-art technology.
本文旨在利用神经网络中的字符嵌入和注意力机制，在花粉过敏监测背景下处理高度非结构化的用户生成内容。目前，花粉热患病率尚无准确的呈现方式，尤其是在实时场景中。社交媒体可作为一种替代方式来提取有关该病症的知识，这对过敏患者、全科医生和政策制定者都很有价值。尽管具有巨大潜力，但传统自然语言处理方法在面对用户生成内容的挑战性本质时显得有限。因此，在大量误报中检测花粉热实例，以及将非专业表述正确识别为花粉过敏症状，构成了一个重大问题。我们提出一种通过字符嵌入和神经注意力增强的深度架构，以提高从推特数据中进行花粉热相关内容分类的性能。由于引入了字符级语义，预测得到了改进，这有效地解决了我们数据集中约9%的词汇外问题。总体而言，该研究朝着利用先进技术从社交媒体改进实时花粉过敏监测迈出了一步。
