Method and Dataset Mining in Scientific Papers

• DOI: 10.48550/arxiv.1911.13096
• Publication Date: 2019-01-01
• Authors: Yao, Rujing; Hou, Linlin; Ye, Yingchun; Wu, Ou; Zhang, Ji; Wu, Jian
• Keywords: FOS: Computer and information sciences; Computer Science - Machine Learning; Computer Science - Computation and Language; Statistics - Machine Learning; 0202 electrical engineering, electronic engineering, information engineering; Machine Learning (stat.ML); 02 engineering and technology; Computation and Language (cs.CL); Information Retrieval (cs.IR); Computer Science - Information Retrieval; Machine Learning (cs.LG)

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.