Hongying Zan

Also published as: Hong-ying Zan


2020

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面向医学文本处理的医学实体标注规范(Medical Entity Annotation Standard for Medical Text Processing)
Huan Zhang (张欢) | Yuan Zong (宗源) | Baobao Chang (常宝宝) | Zhifang Sui (穗志方) | Hongying Zan (昝红英) | Kunli Zhang (张坤丽)
Proceedings of the 19th Chinese National Conference on Computational Linguistics

随着智慧医疗的普及,利用自然语言处理技术识别医学信息的需求日益增长。目前,针对医学实体而言,医学共享语料库仍处于空白状态,这对医学文本信息处理各项任务的进展造成了巨大阻力。如何判断不同的医学实体类别?如何界定不同实体间的涵盖范围?这些问题导致缺乏类似通用场景的大规模规范标注的医学文本数据。针对上述问题,该文参考了UMLS中定义的语义类型,提出面向医学文本信息处理的医学实体标注规范,涵盖了疾病、临床表现、医疗程序、医疗设备等9种医学实体,以及基于规范构建医学实体标注语料库。该文综述了标注规范的描述体系、分类原则、混淆处理、语料标注过程以及医学实体自动标注基线实验等相关问题,希望能为医学实体语料库的构建提供可参考的标注规范,以及为医学实体识别提供语料支持。

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Konwledge-Enabled Diagnosis Assistant Based on Obstetric EMRs and Knowledge Graph
Kunli Zhang | Xu Zhao | Lei Zhuang | Qi Xie | Hongying Zan
Proceedings of the 19th Chinese National Conference on Computational Linguistics

The obstetric Electronic Medical Record (EMR) contains a large amount of medical data and health information. It plays a vital role in improving the quality of the diagnosis assistant service. In this paper, we treat the diagnosis assistant as a multi-label classification task and propose a Knowledge-Enabled Diagnosis Assistant (KEDA) model for the obstetric diagnosis assistant. We utilize the numerical information in EMRs and the external knowledge from Chinese Obstetric Knowledge Graph (COKG) to enhance the text representation of EMRs. Specifically, the bidirectional maximum matching method and similarity-based approach are used to obtain the entities set contained in EMRs and linked to the COKG. The final knowledge representation is obtained by a weight-based disease prediction algorithm, and it is fused with the text representation through a linear weighting method. Experiment results show that our approach can bring about +3.53 F1 score improvements upon the strong BERT baseline in the diagnosis assistant task.

2018

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Detecting Simultaneously Chinese Grammar Errors Based on a BiLSTM-CRF Model
Yajun Liu | Hongying Zan | Mengjie Zhong | Hongchao Ma
Proceedings of the 5th Workshop on Natural Language Processing Techniques for Educational Applications

In the process of learning and using Chinese, many learners of Chinese as foreign language(CFL) may have grammar errors due to negative migration of their native languages. This paper introduces our system that can simultaneously diagnose four types of grammatical errors including redundant (R), missing (M), selection (S), disorder (W) in NLPTEA-5 shared task. We proposed a Bidirectional LSTM CRF neural network (BiLSTM-CRF) that combines BiLSTM and CRF without hand-craft features for Chinese Grammatical Error Diagnosis (CGED). Evaluation includes three levels, which are detection level, identification level and position level. At the detection level and identification level, our system got the third recall scores, and achieved good F1 values.

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Research on Entity Relation Extraction for Military Field
Chen Liang | Hongying Zan | Yajun Liu | Yunfang Wu
Proceedings of the 32nd Pacific Asia Conference on Language, Information and Computation

2016

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Automatic Grammatical Error Detection for Chinese based on Conditional Random Field
Yajun Liu | Yingjie Han | Liyan Zhuo | Hongying Zan
Proceedings of the 3rd Workshop on Natural Language Processing Techniques for Educational Applications (NLPTEA2016)

In the process of learning and using Chinese, foreigners may have grammatical errors due to negative migration of their native languages. Currently, the computer-oriented automatic detection method of grammatical errors is not mature enough. Based on the evaluating task — CGED2016, we select and analyze the classification model and design feature extraction method to obtain grammatical errors including Mission(M), Disorder(W), Selection (S) and Redundant (R) automatically. The experiment results based on the dynamic corpus of HSK show that the Chinese grammatical error automatic detection method, which uses CRF as classification model and n-gram as feature extraction method. It is simple and efficient which play a positive effect on the research of Chinese grammatical error automatic detection and also a supporting and guiding role in the teaching of Chinese as a foreign language.

2012

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A Comparison of Chinese Word Segmentation on News and Microblog Corpora with a Lexicon Based Method
Yuxiang Jia | Hongying Zan | Ming Fan | Zhimin Wang
Proceedings of the Second CIPS-SIGHAN Joint Conference on Chinese Language Processing

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Chinese Personal Name Disambiguation Based on Vector Space Model
Qing-hu Fan | Hong-ying Zan | Yu-mei Chai | Yu-xiang Jia | Gui-ling Niu
Proceedings of the Second CIPS-SIGHAN Joint Conference on Chinese Language Processing

2010

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Studies on Automatic Recognition of Common Chinese Adverb’s usages Based on Statistics Methods
Hongying Zan | Junhui Zhang | Xuefeng Zhu | Shiwen Yu
CIPS-SIGHAN Joint Conference on Chinese Language Processing