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| Move Recognition in Academic Abstract via ERNIE-BiGRU Model |
| WEN Hao1, HE Qianru1, WANG Jie1, QIAO Xiaodong2, ZHANG Peng3 |
1.School of Information and Control Engineering, Xi'an University of Architecture and Technology, Xi'an, Shaanxi 710311, China;
2.Beijing Wanfang Data Co., Ltd, Beijing 100038, China;
3.School of Arts, Xi'an University of Architecture and Technology, Xi'an, Shaanxi 710311, China |
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Abstract The academic abstract summarizes key points in a research paper, with a series of moves conveying different information. The automatic recognition and extraction of moves could provide a valuable foundation for other tasks related with the academic abstract. This paper proposed a move recognition algorithm for academic abstract based on ERNIE-BiGRU model. Firstly, a multi-move structure splitting method based on dependency structure is proposed, identifying the multiple single-move structure in the academic abstract. Secondly, a single-move structure corpus is constructed, and a pre-training model of knowledge-enhanced semantic representation is employed to train sentence-level word vectors. Finally, the trained word vector with single move structure information is input into BiGRU for automatic recognition of moves. The experimental results show that the algorithm has good robustness and high recognition accuracy, achieving 96.57% and 93.75% recognition accuracy for structured and unstructured abstracts, respectively.
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Received: 11 October 2021
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2022, Vol. 36 
