1 National Science Library, Chinese Academy of Sciences, Beijing 100190, China 2 Department of Library Information and Archives Management, University of Chinese Academy of Sciences, Beijing 100190, China 3 Wuhan Library, Chinese Academy of Sciences, Wuhan 430071, China 4 Hubei Key Laboratory of Big Data in Science and Technology, Wuhan 430071, China
[Objective] This paper compares the performance of move recognition methods with different deep learning algorithms. [Methods] Firstly, we built a large training corpus. Then, we used the traditional machine learning method SVM as a benchmark, and developed four moves recognition models based on DNN, LSTM, Attention-BiLSTM and LSTM. Finally, we conducted two rounds of experiments with sample size of 10,000 and 50,000. [Results] Attention-BiLSTM method achieved the best results in both experiments over the four methods (F1=0.9375 with the larger sample). SVM method outperformed DNN and LSTM in both experiments. While changing sample size from 10,000 to 50,000, SVM received the least increase of F1 score (0.0125), and LSTM had the largest increase of F1 score (0.1125). [Limitations] There is no universal test corpus for similar research. Therefore, our results could not be compared with the results of other studies. [Conclusions] The bi-directional LSTM network structure and attention mechanism can significantly improve the performance of move recognition. The deep learning methods work better with larger sample size.
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Zhixiong Zhang,Huan Liu,Liangping Ding,Pengmin Wu,Gaihong Yu. Identifying Moves of Research Abstracts with Deep Learning Methods. Data Analysis and Knowledge Discovery, 2019, 3(12): 1-9.
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