Feature engineering vs. deep learning for paper section identification: Toward applications in Chinese medical literature |
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| Institution: | 1. Centro de Tecnología Biomédica, Universidad Politécnica de Madrid, 28223 Pozuelo de Alarcón, Spain;2. ETS de Ingenieros Informáticos, Universidad Politécnica de Madrid, 28660 Boadilla del Monte, Spain;1. Dept. of Computing and Numerical Analysis University of Córdoba Córdoba, Spain;2. Maimonides Biomedical Research Institute of Cordoba (IMIBIC) Reina Sofia University Hospital, Córdoba, Spain;3. General and Digestive Surgery San Juan de Dios Hospital Córdoba, Spain;1. Department of Computer Information Systems, College of Computer Science and Information Technology, Imam Abdulrahman Bin Faisal University, Dammam 31441, Saudi Arabia;2. King Fahd University Hospital, Imam Abdulrahman Bin Faisal University, Dammam 31441, Saudi Arabia;3. Department of Computer Science, College of Computer Science and Information Technology, Imam Abdulrahman Bin Faisal University, Dammam 31441, Saudi Arabia |
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| Abstract: | Section identification is an important task for library science, especially knowledge management. Identifying the sections of a paper would help filter noise in entity and relation extraction. In this research, we studied the paper section identification problem in the context of Chinese medical literature analysis, where the subjects, methods, and results are more valuable from a physician's perspective. Based on previous studies on English literature section identification, we experiment with the effective features to use with classic machine learning algorithms to tackle the problem. It is found that Conditional Random Fields, which consider sentence interdependency, is more effective in combining different feature sets, such as bag-of-words, part-of-speech, and headings, for Chinese literature section identification. Moreover, we find that classic machine learning algorithms are more effective than generic deep learning models for this problem. Based on these observations, we design a novel deep learning model, the Structural Bidirectional Long Short-Term Memory (SLSTM) model, which models word and sentence interdependency together with the contextual information. Experiments on a human-curated asthma literature dataset show that our approach outperforms the traditional machine learning methods and other deep learning methods and achieves close to 90% precision and recall in the task. The model shows good potential for use in other text mining tasks. The research has significant methodological and practical implications. |
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