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.
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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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Zhou, S., Li, X. (2018). Section Identification to Improve Information Extraction from Chinese Medical Literature. In: Chen, H., Fang, Q., Zeng, D., Wu, J. (eds) Smart Health. ICSH 2018. Lecture Notes in Computer Science(), vol 10983. Springer, Cham. https://doi.org/10.1007/978-3-030-03649-2_34
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