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首页> 外文期刊>Journal of Bioinformatics and Computational Biology >Disease named entity recognition using long-short dependencies
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Disease named entity recognition using long-short dependencies

机译：疾病使用长短依赖性命名实体识别

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摘要

The automatic extraction of disease named entity is a challenging research problem that has attracted attention from the biomedical text mining community. Handcrafted feature methods were employed for this task given a little success since they are limited by the scope of the expert. Lately, deep learning-based methods have been employed to solve this issue. However, most architectures used for this task take into consideration long dependencies only. The proposed method is a two-stage deep neural network model. We start by discovering local dependencies and creating high-level features from word embedding inputs using a deep convolutional neural network. Then we identify long dependencies using a bi-directional recurrent neural network. To solve the problem of unbalanced dataset given by the BMEWO tagging schema and to enforce sequence modeling, we developed a new POS-based tagging schema that subdivides the dominant class into smaller more balanced units. The proposed system was trained and tested on NCBI and achieved an F-score of 85.59 outperforming the current stateof-the-art methods. Our research results show the effectiveness of using both long and short dependencies. The results also illustrate the benefits of combining different word embedding techniques and the incorporation of morphological features in this task.
机译：命名实体的自动提取是一个具有挑战性的研究问题，引起了生物医学文本矿业社区的关注。手工制作的特征方法为这项任务采用了一点成功，因为它们受专家范围的限制。最近，已经采用了深入的学习方法来解决这个问题。但是，对于此任务的大多数体系结构仅考虑了长期依赖性。所提出的方法是一个两级深度神经网络模型。我们首先发现本地依赖关系，并使用深卷积神经网络从嵌入输入中创建高级功能。然后我们使用双向复发性神经网络确定长期依赖关系。为了解决BMEWO标记模式给出的不平衡数据集的问题并强制执行序列建模，我们开发了一种基于新的POS的标记模式，将主导类分为更小的均衡单元。拟议的系统在NCBI培训并测试，并达到了85.59的F分数优于现有的最新方法。我们的研究结果表明了使用长期和短依赖性的有效性。结果还说明了将不同词嵌入技术组合的好处和在这项任务中结合的形态特征。

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