论文标题
图嵌入疾病归一化
Disease Normalization with Graph Embeddings
论文作者
论文摘要
生物医学文本中疾病的检测和归一化是关键的生物医学自然语言处理任务。疾病名称不仅需要确定，而且还需要标准化或与描述网格等疾病的临床分类学相关。在本文中，我们描述了解决这两个任务的深度学习方法。我们训练并测试有关已知NCBI疾病基准语料库的方法。我们建议通过利用网格的图形结构以及使用图形嵌入的分类法中可用的词汇信息来表示疾病名称。我们还表明，通过多任务学习将神经命名实体识别模型与我们的基于图的实体链接方法相结合，从而改善了NCBI语料库的疾病识别。
The detection and normalization of diseases in biomedical texts are key biomedical natural language processing tasks. Disease names need not only be identified, but also normalized or linked to clinical taxonomies describing diseases such as MeSH. In this paper we describe deep learning methods that tackle both tasks. We train and test our methods on the known NCBI disease benchmark corpus. We propose to represent disease names by leveraging MeSH's graphical structure together with the lexical information available in the taxonomy using graph embeddings. We also show that combining neural named entity recognition models with our graph-based entity linking methods via multitask learning leads to improved disease recognition in the NCBI corpus.
