Showcase

In this section, we collect publications, tutorials, and other resources that have used small-text.

Papers

Lukas Kriesch and Sebastian Losacker. 2024.

A global patent dataset of bioeconomy-related inventions

In: Scientific Data Volume 11, Article number: 1308.
David Hanny, Sebastian Schmidt, and Bernd Resch. 2024.

Active Learning for Identifying Disaster-Related Tweets: A Comparison with Keyword Filtering and Generic Fine-Tuning.

In: Intelligent Systems and Applications (IntelliSys 2024). Lecture Notes in Networks and Systems, pages 126–142.
Muhammad Afzal, Jamil Hussain, Asim Abbas, Maqbool Hussain, Muhammad Attique, and Sungyoung Lee. 2024.

Transformer-based active learning for multi-class text annotation and classification.

In: DIGITAL HEALTH Volume 10.

David Kartchner, Irfan Al-Hussaini, Haydn Turner, Jennifer Deng, Shubham Lohiya, Prasanth Bathala, and Cassie S. Mitchell. 2023.

BioSift: A Dataset for Filtering Biomedical Abstracts for Drug Repurposing and Clinical Meta-Analysis.

In: Proceedings of the 46th International ACM SIGIR Conference on Research and Development in Information Retrieval, pages 2913–2923.
Klaus Schmidt, Andreas Niekler, Cathleen Kantner, and Manuel Burghardt. 2023.

Classifying Speech Acts in Political Communication: A Transformer-based Approach with Weak Supervision and Active Learning

In: Proceedings of the 18th Conference on Computer Science and Intelligence Systems, ACSIS, Vol. 35, pages 739–748.

Hannah Kirk, Bertie Vidgen, and Scott Hale. 2022.

Is More Data Better? Re-thinking the Importance of Efficiency in Abusive Language Detection with Transformers-Based Active Learning.

In Proceedings of the Third Workshop on Threat, Aggression and Cyberbullying (TRAC 2022), pages 52–61, Gyeongju, Republic of Korea. Association for Computational Linguistics.
Julius Gonsior, Christian Falkenberg, Silvio Magino, Anja Reusch, Maik Thiele, and Wolfgang Lehner. 2022.

To Softmax, or not to Softmax: that is the question when applying Active Learning for Transformer Models.

ArXiv, abs/2210.03005.
Julia Romberg and Tobias Escher. 2022.

Automated Topic Categorisation of Citizens’ Contributions: Reducing Manual Labelling Efforts Through Active Learning.

In EGOV 2022: Electronic Government, pages 369–385, Cham. Springer International Publishing.
Christopher Schröder, Andreas Niekler, and Martin Potthast. 2022.

Revisiting Uncertainty-based Query Strategies for Active Learning with Transformers.

In Findings of the Association for Computational Linguistics: ACL 2022, pages 2194–2203.