@inproceedings{shnarch-etal-2022-label,
title = "Label Sleuth: From Unlabeled Text to a Classifier in a Few Hours",
author = "Shnarch, Eyal and
Halfon, Alon and
Gera, Ariel and
Danilevsky, Marina and
Katsis, Yannis and
Choshen, Leshem and
Santillan Cooper, Martin and
Epelboim, Dina and
Zhang, Zheng and
Wang, Dakuo",
editor = "Che, Wanxiang and
Shutova, Ekaterina",
booktitle = "Proceedings of the 2022 Conference on Empirical Methods in Natural Language Processing: System Demonstrations",
month = dec,
year = "2022",
address = "Abu Dhabi, UAE",
publisher = "Association for Computational Linguistics",
url = "https://aclanthology.org/2022.emnlp-demos.16",
doi = "10.18653/v1/2022.emnlp-demos.16",
pages = "159--168",
abstract = "Label Sleuth is an open source platform for building text classifiers which does not require coding skills nor machine learning knowledge.- Project website: [\url{https://www.label-sleuth.org/}](\url{https://www.label-sleuth.org/})- Link to screencast video: [\url{https://vimeo.com/735675461}](\url{https://vimeo.com/735675461}){\#}{\#}{\#} AbstractText classification can be useful in many real-world scenarios, saving a lot of time for end users. However, building a classifier generally 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: - being a no-code system, making NLP accessible for non-experts. - guiding its users throughout the entire labeling process until they obtain their desired classifier, making the process efficient - from cold start to a classifier in a few hours. - 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 widen the utilization of NLP models.",
}
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<abstract>Label Sleuth is an open source platform for building text classifiers which does not require coding skills nor machine learning knowledge.- Project website: [https://www.label-sleuth.org/](https://www.label-sleuth.org/)- Link to screencast video: [https://vimeo.com/735675461](https://vimeo.com/735675461)### AbstractText classification can be useful in many real-world scenarios, saving a lot of time for end users. However, building a classifier generally 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: - being a no-code system, making NLP accessible for non-experts. - guiding its users throughout the entire labeling process until they obtain their desired classifier, making the process efficient - from cold start to a classifier in a few hours. - 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 widen the utilization of NLP models.</abstract>
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%0 Conference Proceedings
%T Label Sleuth: From Unlabeled Text to a Classifier in a Few Hours
%A Shnarch, Eyal
%A Halfon, Alon
%A Gera, Ariel
%A Danilevsky, Marina
%A Katsis, Yannis
%A Choshen, Leshem
%A Santillan Cooper, Martin
%A Epelboim, Dina
%A Zhang, Zheng
%A Wang, Dakuo
%Y Che, Wanxiang
%Y Shutova, Ekaterina
%S Proceedings of the 2022 Conference on Empirical Methods in Natural Language Processing: System Demonstrations
%D 2022
%8 December
%I Association for Computational Linguistics
%C Abu Dhabi, UAE
%F shnarch-etal-2022-label
%X Label Sleuth is an open source platform for building text classifiers which does not require coding skills nor machine learning knowledge.- Project website: [https://www.label-sleuth.org/](https://www.label-sleuth.org/)- Link to screencast video: [https://vimeo.com/735675461](https://vimeo.com/735675461)### AbstractText classification can be useful in many real-world scenarios, saving a lot of time for end users. However, building a classifier generally 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: - being a no-code system, making NLP accessible for non-experts. - guiding its users throughout the entire labeling process until they obtain their desired classifier, making the process efficient - from cold start to a classifier in a few hours. - 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 widen the utilization of NLP models.
%R 10.18653/v1/2022.emnlp-demos.16
%U https://aclanthology.org/2022.emnlp-demos.16
%U https://doi.org/10.18653/v1/2022.emnlp-demos.16
%P 159-168
Markdown (Informal)
[Label Sleuth: From Unlabeled Text to a Classifier in a Few Hours](https://aclanthology.org/2022.emnlp-demos.16) (Shnarch et al., EMNLP 2022)
ACL
- Eyal Shnarch, Alon Halfon, Ariel Gera, Marina Danilevsky, Yannis Katsis, Leshem Choshen, Martin Santillan Cooper, Dina Epelboim, Zheng Zhang, and Dakuo Wang. 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.