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      Trend Mining on Bibliographic Data

      proceedings-article

      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.

            Content

            Author and article information

            Contributors
            Conference
            September 2017
            September 2017
            : 1-4
            Affiliations
            [0001]Trier University

            54286 Trier, DE
            Article
            10.14236/ewic/FDIA2017.11
            d3280cb2-fd8c-4f37-9592-3ab450e0b1b3
            © 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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