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| Research on the Identification of Academic Innovation Contributions of Full Academic Texts |
| Zhou Haichen, Zheng Dejun, Li Tianyu |
| College of Information Science & Technology, Nanjing Agricultural University, Nanjing 210095 |
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Abstract In line with recent efforts to dismantle the “SCI supremacy” phenomenon, automatic identification of the innovation and contribution of academic research could greatly improve and support both peer review and evaluation of representative work. This study proposes a method for recognizing academic innovation and contribution based on a combination of deep learning and rules. First, we identify the potential phrases related to innovation and scientific contribution in academic texts. Second, we build an automatic recognition model based on BERT, develop fine-grained extraction rules, and apply them to extraction of large-scale data sets. To empirically test the method, we applied it to evaluate research texts in the field of chrysanthemums, and were able to successfully identify and extract potential academic innovation and contributions. Future study could expand this method to visualize innovation and contribution by specific scholars.
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Received: 04 May 2020
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