51. Moves Recognition in Abstract of Research Paper Based on Deep Learning
- Author
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Zhixiong Zhang, Gaihong Yu, Liangping Ding, Huan Liu, and Pengmin Wu
- Subjects
Artificial neural network ,business.industry ,Computer science ,Deep learning ,Sample (statistics) ,Paper based ,Machine learning ,computer.software_genre ,Support vector machine ,Sample size determination ,Benchmark (computing) ,Artificial intelligence ,business ,computer - Abstract
The purpose of this work is to explore the applicability and effectiveness of deep learning methods for the task------moves recognition in abstract of research paper. We firstly build a large corpus for moves recognition. Then we choose the traditional machine learning method SVM as a benchmark, and develop four moves recognition methods based on DNN, LSTM, Attention-BiLSTM and BERT. Finally, we design two groups of experiments with sample size 10,000 and 50,000 and then compare experimental results. The results show that most of the deep learning methods outperform the traditional machine learning method SVM especially in large-scale sample experiments, in which the BERT with a re-pre-trained model achieves the best results in both groups of experiments. Deep learning methods are proved applicable and effective for moves recognition in research paper abstracts.
- Published
- 2019