BACKGROUND Clinical Named Entity Recognition is to find the name of diseases, body parts and other related terms from the given text. Because Chinese language is quite different with English language, the machine cannot simply get the graphical and phonetic information form Chinese characters. The method for Chinese should be different from that for English. Chinese characters present abundant information with the graphical features, recent research on Chinese word embedding tries to use graphical information as subword. This paper uses both graphical and phonetic features to improve Chinese Clinical Named Entity Recognition based on the presence of phono-semantic characters. METHODS This paper proposed three different embedding models and tested them on the annotated data. The data have been divided into two sections for exploring the effect of the proportion of phono-semantic characters. RESULTS The model using primary radical and pinyin can improve Clinical Named Entity Recognition in Chinese and get the F-measure of 0.712. More phono-semantic characters does not give a better result. CONCLUSIONS The paper proves that the use of the combination of graphical and phonetic features can improve the Clinical Named Entity Recognition in Chinese.

中文翻译：

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使用图形和语音功能改善中文的临床命名实体识别。

背景技术临床命名实体识别是从给定的文本中找到疾病，身体部位和其他相关术语的名称。因为中文和英文有很大的不同，所以机器不能简单地从汉字中获取图形和语音信息。中文的方法应与英语的方法不同。汉字具有图形化特征，提供了丰富的信息，最近有关汉字嵌入的研究试图将图形化信息用作子词。本文利用图形和语音特征，基于语音语义特征的存在来改进中文临床命名实体识别。方法本文提出了三种不同的嵌入模型，并在带注释的数据上对其进行了测试。数据已分为两个部分，以探讨语音语义字符比例的影响。结果采用基音和拼音的模型可以提高中文的临床命名实体识别能力，F值达到0.712。更多的语音语义字符不会提供更好的结果。结论本文证明了结合使用图形和语音特征可以改善中文的临床命名实体识别。