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      Trend Mining on Bibliographic Data

      Seventh BCS-IRSG Symposium on Future Directions in Information Access (FDIA)

      Future Directions in Information Access

      5 September 2017

      Trend Mining, NLP, Machine Learning

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          Abstract

          Most times, current and emerging trends in scientific communities are only discernible for active domain experts. Trend mining on bibliographic data may give insight for politicians, entrepreneurs and new scientists. This work outlines a research agenda for approaching this problem.

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          Using ‘core documents’ for detecting and labelling new emerging topics

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            Detection of Embryonic Research Topics by Analysing Semantic Topic Networks

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              Author and article information

              Contributors
              Conference
              September 2017
              September 2017
              : 1-4
              Affiliations
              Trier University

              54286 Trier, DE
              Article
              10.14236/ewic/FDIA2017.11
              © Kreutz. Published by BCS Learning and Development Ltd. Proceedings of the 7th Symposium on Future Directions in Information Access 2017

              This work is licensed under a Creative Commons Attribution 4.0 Unported License. To view a copy of this license, visit http://creativecommons.org/licenses/by/4.0/

              Seventh BCS-IRSG Symposium on Future Directions in Information Access
              FDIA
              7
              Barcelona, Spain
              5 September 2017
              Electronic Workshops in Computing (eWiC)
              Future Directions in Information Access
              Product
              Product Information: 1477-9358BCS Learning & Development
              Self URI (journal page): https://ewic.bcs.org/
              Categories
              Electronic Workshops in Computing

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