@inproceedings{0fd701207168420f85883406b0f178c2,
title = "Moves recognition in abstract of research paper based on deep learning",
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.",
keywords = "Deep Learning, Moves Recognition, Neural Network, Research Paper",
author = "Zhixiong Zhang and Huan Liu and Liangping Ding and Pengmin Wu and Gaihong Yu",
note = "Publisher Copyright: {\textcopyright} 2019 IEEE.; 19th ACM/IEEE Joint Conference on Digital Libraries, JCDL 2019 ; Conference date: 02-06-2019 Through 06-06-2019",
year = "2019",
month = jun,
doi = "10.1109/JCDL.2019.00085",
language = "English",
series = "Proceedings of the ACM/IEEE Joint Conference on Digital Libraries",
publisher = "IEEE",
pages = "390--391",
editor = "Maria Bonn and Dan Wu and Downie, {Stephen J.} and Alain Martaus",
booktitle = "Proceedings - 2019 ACM/IEEE Joint Conference on Digital Libraries, JCDL 2019",
address = "United States",
}