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| Automatic Recognition of Research Methods from the Full-text of Academic Articles |
| Zhang Chengzhi, Zhang Yingyi |
| Department of Information Management, School of Economics and Management, Nanjing University of Science & Technology, Nanjing 210094 |
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Abstract The degree ofresearch methods standardization marks the maturity of a discipline s development. In information science, theoretical analysis and normative research has gradually started to attract the attention of researchers. However, there is a lack of research on quantitative analyses of research methods. In addition, when a research method appears in an academic article, this implies that either the research method is used in the article, or it is just cited for analysis or comparison. Through research methods, researchers can quickly understand the key contents of the academic articles. Summarizing the research methods cited by academic papers helps in clarifying their evolution and development mode in the field. Thus, this paper divides research methods into those reported by and those cited in academic articles. First, this article compares a variety of automatic named entity recognition method, such as BiLSTM (bi-directional long short-term memory), from which an optimal model for final research method entities identification would be selected. The experimental results show that the character vector based BiLSTM joint training model combined with a CRF (conditional random field) yields the best performance. This paper analyzes research methods’ use in information science through the extracted research method entities. The results of statistical analysis show that the usage and citation frequency of experimental methods is the highest in information science.
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Received: 13 October 2019
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