Moves recognition in abstract of research paper based on deep learning

Zhixiong Zhang, Huan Liu, Liangping Ding, Pengmin Wu, Gaihong Yu

Research output: Chapter in Book/Report/Conference proceedingConference contributionpeer-review

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.

Original languageEnglish
Title of host publicationProceedings - 2019 ACM/IEEE Joint Conference on Digital Libraries, JCDL 2019
EditorsMaria Bonn, Dan Wu, Stephen J. Downie, Alain Martaus
PublisherIEEE
Pages390-391
Number of pages2
ISBN (Electronic)9781728115474
DOIs
Publication statusPublished - Jun 2019
Event19th ACM/IEEE Joint Conference on Digital Libraries, JCDL 2019 - Urbana-Champaign, United States
Duration: 2 Jun 20196 Jun 2019

Publication series

NameProceedings of the ACM/IEEE Joint Conference on Digital Libraries
Volume2019-June
ISSN (Print)1552-5996

Conference

Conference19th ACM/IEEE Joint Conference on Digital Libraries, JCDL 2019
Country/TerritoryUnited States
CityUrbana-Champaign
Period2/06/196/06/19

Keywords

  • Deep Learning
  • Moves Recognition
  • Neural Network
  • Research Paper

Research Beacons, Institutes and Platforms

  • Manchester Institute of Innovation Research

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