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.
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. InProceedings of the 2022 Conference on Empirical Methods in Natural Language Processing: System Demonstrations, pages 159–168, Abu Dhabi, UAE. Association for Computational Linguistics.
@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 and Yip, Lucy and Ein-Dor, Liat and Dankin, Lena and Shnayderman, Ilya and Aharonov, Ranit and Li, Yunyao and Liberman, Naftali and Levin Slesarev, Philip and Newton, Gwilym and Ofek-Koifman, Shila and Slonim, Noam and Katz, Yoav", 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."}
%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%A Yip, Lucy%A Ein-Dor, Liat%A Dankin, Lena%A Shnayderman, Ilya%A Aharonov, Ranit%A Li, Yunyao%A Liberman, Naftali%A Levin Slesarev, Philip%A Newton, Gwilym%A Ofek-Koifman, Shila%A Slonim, Noam%A Katz, Yoav%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
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. InProceedings of the 2022 Conference on Empirical Methods in Natural Language Processing: System Demonstrations, pages 159–168, Abu Dhabi, UAE. Association for Computational Linguistics.