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| Identifying Moves in Full-Text Chinese Academic Papers |
Du Xinyu( ),Li Ning |
| Computer School, Beijing Information Science & Technology University, Beijing 100101, China |
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Abstract [Objective] This paper investigates the recognition of moves in full-text academic papers. It establishes a solid foundation for automatically understanding paper contents. Existing research on move recognition in academic papers only processes a small number of moves with coarse granularity. There are few open datasets for move classification. [Methods] Based on the BERT model, we constructed a move classification dataset of academic papers with multi-stage fine-tuning. Then, we proposed a move recognition model incorporating the section titles to recognize moves at a fine-grained level. [Results] For the 22-class classification, the overall accuracy of the RoBERTa-wwm-ext model increased by 0.031 to 0.909, and the Micro-F1 improved by 0.022 to 0.837. [Limitations] There is a small amount of unbalanced data in the constructed corpus, and the paper's quality will affect by the proposed model's performance. [Conclusions] The proposed model benefits the automatic understanding of academic papers, research quality evaluation, and semantic content retrieval, which play important roles in using scientific and technological literature.
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Received: 04 December 2022
Published: 28 March 2023
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| Fund:National Natural Science Foundation of China(61672105) |
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Corresponding Authors:
Du Xinyu，ORCID：0000-0001-5289-8199，E-mail： duxinyu_0@163.com。
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