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J Biomed Inform. 2017 May;69:203-217. doi: 10.1016/j.jbi.2017.04.006. Epub 2017 Apr 9.

Building a comprehensive syntactic and semantic corpus of Chinese clinical texts.

Author information

1
School of Computer Science and Technology, Harbin Institute of Technology, Harbin, China.
2
Ricoh Software Research Center (Beijing), Beijing, China.
3
School of Computer Science and Technology, Harbin Institute of Technology, Harbin, China. Electronic address: guanyi@hit.edu.cn.
4
School of Software, Harbin University of Science and Technology, Harbin, China.
5
Medical Records Room, The Second Affiliated Hospital of Harbin Medical University, Harbin, China.

Abstract

OBJECTIVE:

To build a comprehensive corpus covering syntactic and semantic annotations of Chinese clinical texts with corresponding annotation guidelines and methods as well as to develop tools trained on the annotated corpus, which supplies baselines for research on Chinese texts in the clinical domain.

MATERIALS AND METHODS:

An iterative annotation method was proposed to train annotators and to develop annotation guidelines. Then, by using annotation quality assurance measures, a comprehensive corpus was built, containing annotations of part-of-speech (POS) tags, syntactic tags, entities, assertions, and relations. Inter-annotator agreement (IAA) was calculated to evaluate the annotation quality and a Chinese clinical text processing and information extraction system (CCTPIES) was developed based on our annotated corpus.

RESULTS:

The syntactic corpus consists of 138 Chinese clinical documents with 47,426 tokens and 2612 full parsing trees, while the semantic corpus includes 992 documents that annotated 39,511 entities with their assertions and 7693 relations. IAA evaluation shows that this comprehensive corpus is of good quality, and the system modules are effective.

DISCUSSION:

The annotated corpus makes a considerable contribution to natural language processing (NLP) research into Chinese texts in the clinical domain. However, this corpus has a number of limitations. Some additional types of clinical text should be introduced to improve corpus coverage and active learning methods should be utilized to promote annotation efficiency.

CONCLUSIONS:

In this study, several annotation guidelines and an annotation method for Chinese clinical texts were proposed, and a comprehensive corpus with its NLP modules were constructed, providing a foundation for further study of applying NLP techniques to Chinese texts in the clinical domain.

KEYWORDS:

Annotation method; Chinese clinical texts; Corpus construction; Guideline development; Natural language processing

PMID:
28404537
DOI:
10.1016/j.jbi.2017.04.006
[Indexed for MEDLINE]
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