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 to non-experts, (b) guiding users through the entire labeling process until they obtain a custom classifier, making the process efficient -- from cold start to 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.
@inproceedings{DBLP:conf/emnlp/ShnarchHGDKCCEZW22,
author = {Eyal Shnarch and
Alon Halfon and
Ariel Gera and
Marina Danilevsky and
Yannis Katsis and
Leshem Choshen and
Mart{\'{\i}}n Santill{\'{a}}n Cooper and
Dina Epelboim and
Zheng Zhang and
Dakuo Wang},
editor = {Wanxiang Che and
Ekaterina Shutova},
title = {Label Sleuth: From Unlabeled Text to a Classifier in a Few Hours},
booktitle = {Proceedings of the The 2022 Conference on Empirical Methods in Natural
Language Processing, {EMNLP} 2022 - System Demonstrations, Abu Dhabi,
UAE, December 7-11, 2022},
pages = {159--168},
publisher = {Association for Computational Linguistics},
year = {2022},
url = {https://doi.org/10.18653/v1/2022.emnlp-demos.16},
doi = {10.18653/V1/2022.EMNLP-DEMOS.16},
timestamp = {Mon, 22 Jul 2024 01:00:00 +0200},
biburl = {https://dblp.org/rec/conf/emnlp/ShnarchHGDKCCEZW22.bib},
bibsource = {dblp computer science bibliography, https://dblp.org}
}
See the full reference list in the paper.