Construction of Chinese Medical Text Segmentation Model Based on Bi-GRU Algorithm

Abstract

Word segmentation is a basic task of natural language processing, whose purpose is to correctly segment the text according to the context. Due to the vagueness, classical language, word order fixation, and unstructured characteristics of Traditional Chinese Medicine (TCM) text, the problem of word segmentation has not been effectively solved. This paper uses 20, 000 TCM text collected from the Chinese medicine clinic of the Second Affiliated Hospital of Shandong University of Traditional Chinese Medicine from 2005 to 2020 as the data set, data set source URL: http://www.sdmlzy.com. By labeling the four-word position of the characters in the text of TCM text, the word2vec is applied, by using the Long Short-Term Memory (LSTM) variant bidirectional gate recurring unit Gated Recurrent Unit (GRU) and then using Viterbi algorithm to achieve the resolution of TCM text word segmentation. Experimental results show that the word segmentation model proposed in this paper simplifies the gate structure based on inheriting automatic learning features and using contextual information. The model is applied to the word segmentation of Chinese medicine text corpus, and the precision rate reached 93.26%.