Research Area:  Machine Learning

Abstract:

China has entered a stage of high-quality development, and people have higher demand and expectations for the existing medical field. MNER(Medical Named Entity Recognition) is the task of identifying the correct entity boundary and classifying medical entities from a piece of medical text information. The effect of MNER would directly affect the performance of downstream relationship extraction and intelligent question answering, which has important research significance and value. In this paper, aiming at the named entity recognition of discontinuous medical entities in Chinese medical text information, this paper expands the research based on BiLSTM-CRF, introduces the IDCNN layer after the input layer of the model to capture the local context information in the medical text, and then uses the output of IDCNN as the input of BiLSTM-CRF for subsequent training, to construct an IDCNN-BiLSTM-CRF network model for discontinuous medical text. Based on BERT, the weight is assigned to the 12-layer transformer in BERT, and the results are weighted and averaged, and a WfBERT-Att-D-BiLSTM-CRF model based on the weight output of different layers of BERT is proposed. During the experiment, the hidden layer, the number of iterations, and the batch data size are continuously experimented with, and finally, the optimal parameter settings are obtained. In this paper, repeated experiments are carried out on different datasets, the final actual recognition effect is also tested, and the correctness of the proposed model is verified from different perspectives.

Keywords:  

Author(s) Name:  Qinlu He, Pengze Gao, Fan Zhang, Genqing Bian, Zhen Li, Zan Wang

Publisher name:  Springer

DOI:  10.1007/s11042-023-16900-x

Volume Information:  Volume 83, pages 32739–32763, (2024)

Paper Link:   https://link.springer.com/article/10.1007/s11042-023-16900-x