Extraction and Evaluation of Knowledge Entities from Scientific Documents
Author:
Affiliation:
1. Nanjing University of Science and Technology , China
2. GESIS - Leibniz-Institute for the Social Sciences , Germany
3. Wuhan University , China
4. University of Technology Sydney (UTS) , Australia
Publisher
Walter de Gruyter GmbH
Link
https://www.sciendo.com/pdf/10.2478/jdis-2021-0025
Reference28 articles.
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2. Belter, C.W. (2014). Measuring the value of research data: A citation analysis of oceanographic data sets. PloS One, 9(3), Article e92590. https://doi.org/10.1371/journal.pone.0092590
3. Cabanac, G., Chandrasekaran, M., Frommholz, I., Jaidka, K., Kan, M., Mayr, P., & Wolfram, D. (2016). Report on the Joint Workshop on Bibliometric-enhanced Information Retrieval and Natural Language Processing for Digital Libraries (BIRNDL 2016). SIGIR Forum, 50(2), 36–43.
4. Chandrasekaran, M.K., de Waard, A., Feigenblat, G., Freitag, D., Ghosal, T., Hovy, E., & Shmueli-Scheuer, M. (2020, November). Proceedings of the first workshop on scholarly document processing. Retrieved from https://www.aclweb.org/anthology/volumes/2020.sdp-1/
5. Chen H. (2021). A New Citation Recommendation Strategy Based on Term Functions in Related Studies Section. Journal of Data and Information Science, 6(3), 75–98. https://doi.org/10.2478/jdis-2021-0022
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