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Section Identification to Improve Information Extraction from Chinese Medical Literature

  • Sijia ZhouEmail author
  • Xin Li
Conference paper
Part of the Lecture Notes in Computer Science book series (LNCS, volume 10983)

Abstract

The Chinese medical literature contains a large amount of knowledge. Reducing the effort needed by medical scholars to extract this knowledge requires a literature analysis to identify the key information in each paper. We argue that identifying the sections of a paper would help us filter noise from the paper and increase the accuracy of extracting the experimental findings. In this research in progress, we consider paper section identification as a sentence classification task and apply Conditional Random Fields (CRFs) to tackle the problem. In our model we combine both lexical and structural features to facilitate section identification. Experiments on a human-curated asthma dataset show that our approach achieves a 10%–20% performance improvement over Support Vector Machines (SVMs), and that use of both bag-of-words features and domain lexicons benefit the task.

Keywords

Section identification Sentence classification Chinese medicine 

Notes

Acknowledgements

The research is partially supported by Digital Innovation Lab at City University of Hong Kong, GuangDong Science and Technology Project 2014A020221090, and the City University of Hong Kong Shenzhen Research Institute.

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Copyright information

© Springer Nature Switzerland AG 2018

Authors and Affiliations

  1. 1.Department of Information SystemsCity University of Hong KongKowloonHong Kong
  2. 2.Shenzhen Research InstituteCity University of Hong KongShenzhenChina

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