Method and Dataset Mining in Scientific Papers

11/29/2019 ∙ by Rujing Yao, et al. ∙ 0 ∙ share

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.

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Method and Dataset Mining in Scientific Papers

Authors

Method and Dataset Entity Mining in Scientific Literature: A CNN + Bi-LSTM Model with Self-attention

AI Marker-based Large-scale AI Literature Mining

Creativity in Science and the Link to Cited References: Is the Creative Potential of Papers Reflected in their Cited References?

A Recommendation System of Grants to Acquire External Funds

AxCell: Automatic Extraction of Results from Machine Learning Papers

A Graph Analytics Framework for Ranking Authors, Papers and Venues

Compiler Testing: A Systematic Literature Analysis

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