Suchetha is a research associate at KMi, part of the Big Scientific Data and Text Analytics Group (BSDTAG) team: http://bsdtag.kmi.open.ac.uk/. She finished her PhD on "Language Models for Citation Classification." Her current research focuses on the author's affiliation extraction from scientific meta-data and classification of scholarly publications based on the UN Sustainable Development Goals.

Team: Valeriy Budko, Matteo Cancellieri, Ekaterine Chkhaidze, Petr Knoth, Viktoriia Pavlenko, David Pride, Andrew Vasilyev, Anton Zhuk

CORE receives funding for five years from Microsoft’s open data initiative to increase access to open scientific research

Fostering Synergies: C&C and KMi research & innovation event

KMi Congratulates Dr. Suchetha Nambanoor-Kunnath for Completing her PhD

CORE Membership launching soon!

NISO vision interview with CORE’s Petr Knoth on the role of text mining in scholarly communication

N. Kunnath, S., Pride, D. and Knoth, P. (2023) Prompting Strategies for Citation Classification, Proceedings of the 32nd ACM International Conference on Information and Knowledge Management (CIKM '23), Birmingham, United Kingdom

N. Kunnath, S., Pride, D. and Knoth, P. (2022) Dynamic Context Extraction for Citation Classification, The 2nd Conference of the Asia-Pacific Chapter of the Association for Computational Linguistics and the 12th International Joint Conference on Natural Language Processing, Virtual

N. Kunnath, S., Stauber, V., Wu, R., Pride, D., Botev, V. and Knoth, P. (2022) ACT2: A multi-disciplinary semi-structured dataset for importance and purpose classification of citations, Proceedings of the 13th Language Resources and Evaluation Conference, Marseille

N. Kunnath, S., Pride, D., Herrmannova, D. and Knoth, P. (2021) Overview of the 2021 SDP 3C Citation Context Classification Shared Task, Second Workshop on Scholarly Document Processing (SDP), Mexico City, Mexico

N. Kunnath, S., Herrmannova, D., Pride, D. and Knoth, P. (2021) A Meta-analysis of Semantic Classification of Citations, Quantitative Science Studies, pp. (Early access)