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Method and Dataset Mining in Scientific Papers

Authors :
Rujing Yao
Ji Zhang
Linlin Hou
Jian Wu
Yingchun Ye
Source :
IEEE BigData
Publication Year :
2019
Publisher :
IEEE, 2019.

Abstract

Literature analysis facilitates researchers better understanding the development of science and technology. The conventional literature analysis focuses on the topics, authors, abstracts, keywords, references, etc., and rarely pays attention to the content of papers. In the field of machine learning, the involved methods (M) and datasets (D) are key information in papers. The extraction and mining of M and D are useful for discipline analysis and algorithm recommendation. In this paper, we propose a novel entity recognition model, called MDER, and constructe datasets from the papers of the PAKDD conferences (2009-2019). Some preliminary experiments are conducted to assess the extraction performance and the mining results are visualized.

Details

Database :
OpenAIRE
Journal :
2019 IEEE International Conference on Big Data (Big Data)
Accession number :
edsair.doi...........01378075cc1bc8588a716da9b1e390a9