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| Using Full Content to Automatically Classify the Research Methods of Academic Articles |
| Zhang Chengzhi1, Li Zhuo1, Chu Heting2 |
1.Department of Information Management, School of Economics and Management, Nanjing University of Science & Technology, Nanjing 210094
2.Palmer School of Library and Information Science, Long Island University, New York 11548 |
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Abstract Automatic classification of the research methods used in academic papers is helpful for the evaluative analysis of these research methods in that it provides a basis for researchers to recommend or select the appropriate methods for their scholarly endeavors. Compared with using only abstracts for classification, the full content of articles contain more context regarding research methods, which is of great significance in exploring such automatic classification. This study examines the full content of 820 academic papers in the field of library and information science (LIS). Experts in the field of the LIS annotated method went through these academic papers. Subsequently, a training corpus for the classification of research methods was generated. We adopted the problem transformation method and algorithm adaptive method in the multi-label classification task. Na?ve Bayes and Support Vector Machine were used as the underlying classifiers of the problem transformation method to construct six different classification models. Meanwhile, the ML-KNN model in the algorithm adaptive method was selected to automatically classify the research methods used in the chosen articles. The experimental results showed that classification performance with the full article improved greatly when compared to using only the abstract. The Na?ve Bayes algorithm performed the best in the classifier chain strategy of the problem transformation method, and the F1 value reached 0.705. In addition, the results also demonstrated that research methods used in different academic papers are represented differently. A small training set would lead to low generalizability of automatic classification results.
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Received: 15 April 2019
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