Identifying Moves in Full-Text Chinese Academic Papers
[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-Fl 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.
Academic Papers UnderstandingMove RecognitionPre-trained Model
NETL
NSTL
万方数据
