文献类型：期刊文献

英文题名：Active learning for Chinese word segmentation in medical text

作者：Cai, Tingting[1]; Zhou, Yangming[1]; Ma, Zhiyuan[1]; Zheng, Hong[1]; Zhang, Lingfei[1]; He, Ping[2]; Gao, Ju[3]

机构：[1] School of Information Science and Engineering, East China University of Science and Technology, Shanghai, 200237, China; [2] Shanghai Hospital Development Center, Shanghai, 200041, China; [3] Shuguang Hospital Affiliated to Shanghai University of Traditional Chinese Medicine, Shanghai, 200021, China

年份：2019

外文期刊名：arXiv

收录：EI(收录号：20200209397)

语种：英文

外文关键词：Data mining - Deep learning - E-learning - Hospitals - K-means clustering - Learning algorithms - Medical computing - Natural language processing systems - Patient treatment - Records management

摘要：Electronic health records (EHRs) stored in hospital information systems completely reflect the patients' diagnosis and treatment processes, which are essential to clinical data mining. Chinese word segmentation (CWS) is a fundamental and important task for Chinese natural language processing. Currently, most state-of-the-art CWS methods greatly depend on large-scale manually-annotated data, which is a very time-consuming and expensive work, specially for the annotation in medical field. In this paper, we present an active learning method for CWS in medical text. To effectively utilize complete segmentation history, a new scoring model in sampling strategy is proposed, which combines information entropy with neural network. Besides, to capture interactions between adjacent characters, K-means clustering features are additionally added in word segmenter. We experimentally evaluate our proposed CWS method in medical text, experimental results based on EHRs collected from the Shuguang Hospital Affiliated to Shanghai University of Traditional Chinese Medicine show that our proposed method outperforms other reference methods, which can effectively save the cost of manual annotation. Copyright ? 2019, The Authors. All rights reserved.