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Method and Dataset Entity Mining in Scientific Literature: A CNN + Bi-LSTM Model with Self-attention
Literature analysis facilitates researchers to acquire a good understand...
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AI Marker-based Large-scale AI Literature Mining
The knowledge contained in academic literature is interesting to mine. I...
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Creativity in Science and the Link to Cited References: Is the Creative Potential of Papers Reflected in their Cited References?
Several authors have proposed that a large number of unusual combination...
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A Recommendation System of Grants to Acquire External Funds
The recommendation system of the competitive grants to university resear...
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AxCell: Automatic Extraction of Results from Machine Learning Papers
Tracking progress in machine learning has become increasingly difficult ...
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A Graph Analytics Framework for Ranking Authors, Papers and Venues
A lot of scientific works are published in different areas of science, t...
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Compiler Testing: A Systematic Literature Analysis
Compilers are widely-used infrastructures in accelerating the software d...
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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, 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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