融合嵌入字词特征的中文医疗命名实体识别

针对中文医疗文献中的中文词边界模糊、分词歧义导致传统深度学习方法难以获取词汇语义信息的问题,提出了一种融合嵌入字词特征的中文医疗命名实体识别模型.首先,针对词向量缺失边界特征的问题,将词向量与词性、词边界特征拼接融合,结合注意力机制捕获字符间潜在的依赖权重等特征和增强词汇向量;其次,将通过BERT模型获得的字符向量与增...

Recognition of Chinese-named medical entities embedded words character

A recognition model of Chinese-named medical entities embedded character characteristics was proposed according to the difficult access to lexical semantic information due to the fuzzy boundary of Chinese words and traditional deep learning method caused by word segment ambiguity in Chinese medical literature. The potential dependence weight between characters was captured and the reinforced word sector was established by joining and embedding the word sector into the word property and word boundary characteristics in combination with the attention mechanism. The context semantic information characteristics were then extracted by making use of the BiLSTM model based on the joined and embedded vocabulary vector and reinforced word sector established by making use of the BERT model used as embedded characters. The sequence was finally decoded by making use of the CRF model. The recognition model of Chinese-named medical entities embedded character characteristics achieved quite good results by making use of the MMC-labelled diabetic data in Ruijin Hospital.