A Fusion Model for Chinese Electronic Medical Record Parsing

Full parsing is an important structuring process of the natural language processing (NLP). However, its research on Chinese electronic medical record (CEMR) is currently a blank because of the lack of syntactical annotated corpus on CEMR. To make the best of the sub-language characteristic of strong pattern in CEMR, patterns reused is first formalized as tree fragment in CEMR, and a model integrating data-oriented parsing (DOP) and hierarchical parsing is proposed. In the extraction stage of tree fragments, we propose a more efficient standard tree fragment algorithm by solving repeated comparison of standard tree fragments, and a partial tree fragment extraction algorithm to substitute for the low-efficient quadratic tree kernel (QTK) algorithm to obtain a standard tree fragment set and a partial tree fragment set. Based on the two extracted tree fragment sets, a strategy matching word and part-of-speech (POS) synchronously and a maximal combination algorithm of tree fragments are proposed to improve DOP, and alleviate the noise caused by invalid tree fragments. Experimental results show that the fusion model based on DOP and hierarchical parsing can effectively improve the parsing effect for CEMR, and the F1 score reaches the highest 80.87 % based on a small number of annotated corpora, which is even 2 % higher than those of the two state-of-the-art parsers of Stanford and Berkeley in cross-department parsing. Copyright © 2019 Acta Automatica Sinica. All rights reserved.