Label Sleuth: From Unlabeled Text to a Classifier in a Few Hours

Eyal Shnarch, Alon Halfon, Ariel Gera, Marina Danilevsky, Yannis Katsis, Leshem Choshen, Martin Santillan Cooper, Dina Epelboim, Zheng Zhang, Dakuo Wang, Lucy Yip, Liat Ein-Dor, Lena Dankin, Ilya Shnayderman, Ranit Aharonov, Yunyao Li, Naftali Liberman, Philip Levin Slesarev, Gwilym Newton, Shila Ofek-Koifman, Noam Slonim, Yoav Katz


Abstract
Text classification can be useful in many real-world scenarios, saving a lot of time for end users. However, building a custom classifier typically requires coding skills and ML knowledge, which poses a significant barrier for many potential users. To lift this barrier we introduce Label Sleuth, a free open source system for labeling and creating text classifiers. This system is unique for: (a) being a no-code system, making NLP accessible for non-experts, (b) guiding its users throughout the entire labeling process until they obtain a custom classifier, making the process efficient – from cold start to a classifier in a few hours, and (c) being open for configuration and extension by developers. By open sourcing Label Sleuth we hope to build a community of users and developers that will broaden the utilization of NLP models.
Anthology ID:
2022.emnlp-demos.16
Volume:
Proceedings of the 2022 Conference on Empirical Methods in Natural Language Processing: System Demonstrations
Month:
December
Year:
2022
Address:
Abu Dhabi, UAE
Editors:
Wanxiang Che, Ekaterina Shutova
Venue:
EMNLP
SIG:
Publisher:
Association for Computational Linguistics
Note:
Pages:
159–168
Language:
URL:
https://preview.aclanthology.org/automate-pypi-publishing/2022.emnlp-demos.16/
DOI:
10.18653/v1/2022.emnlp-demos.16
Bibkey:
Cite (ACL):
Eyal Shnarch, Alon Halfon, Ariel Gera, Marina Danilevsky, Yannis Katsis, Leshem Choshen, Martin Santillan Cooper, Dina Epelboim, Zheng Zhang, Dakuo Wang, Lucy Yip, Liat Ein-Dor, Lena Dankin, Ilya Shnayderman, Ranit Aharonov, Yunyao Li, Naftali Liberman, Philip Levin Slesarev, Gwilym Newton, Shila Ofek-Koifman, Noam Slonim, and Yoav Katz. 2022. Label Sleuth: From Unlabeled Text to a Classifier in a Few Hours. In Proceedings of the 2022 Conference on Empirical Methods in Natural Language Processing: System Demonstrations, pages 159–168, Abu Dhabi, UAE. Association for Computational Linguistics.
Cite (Informal):
Label Sleuth: From Unlabeled Text to a Classifier in a Few Hours (Shnarch et al., EMNLP 2022)
Copy Citation:
PDF:
https://preview.aclanthology.org/automate-pypi-publishing/2022.emnlp-demos.16.pdf
Video:
 https://preview.aclanthology.org/automate-pypi-publishing/2022.emnlp-demos.16.mp4