Method and Dataset Mining in Scientific Papers

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...

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Bibliographic Details
Main Authors Yao, Rujing, Hou, Linlin, Ye, Yingchun, Wu, Ou, Zhang, Ji, Wu, Jian
Format Journal Article
LanguageEnglish
Published 29.11.2019
Subjects
Computer Science - Computation and Language
Computer Science - Information Retrieval
Computer Science - Learning
Statistics - Machine Learning
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Summary: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.
DOI:10.48550/arxiv.1911.13096