Short Text Classification of EMR Based on Entities and Dependency Parser

Nowadays, text classification and text mining of Electronic Medical Record(EMR) have become the basis of the Big Data research in biomedical fields. This paper proposes a method using entity dictionaries and dependency parser as the feature to do the classification of short texts in EMR. It used NLP to preprocess the texts first including sentence segmentation, word segmentation, part of speech and entity extraction. Then several entity dictionaries were built according to the result of NLP. After that the TF-IDF and LSA were deployed to select the vocabulary feature. Then considering the characters of EMR, dependency parser was done to the texts and triple dependency relation features would be used as the expanding feature for text classification. The result of the experiment shows that comparing to the classification which used vocabulary features only, the proposed method can effectively improve the performance of classifier and the precision and F-value are obviously higher.