TY - GEN
T1 - ALAMBIC
T2 - 17th Conference of the European Chapter of the Association for Computational Linguistics, EACL 2023
AU - Nachtegael, Charlotte
AU - De Stefani, Jacopo
AU - Lenaerts, Tom
N1 - Green Open Access added to TU Delft Institutional Repository ‘You share, we take care!’ – Taverne project https://www.openaccess.nl/en/you-share-we-take-care Otherwise as indicated in the copyright section: the publisher is the copyright holder of this work and the author uses the Dutch legislation to make this work public.
PY - 2023
Y1 - 2023
N2 - In this paper, we present ALAMBIC, an open-source dockerized web-based platform for annotating text data through active learning for classification tasks. Active learning is known to reduce the need of labelling, a time-consuming task, by selecting the most informative instances among the unlabelled instances, reaching an optimal accuracy faster than by just randomly labelling data. ALAMBIC integrates all the steps from data import to customization of the (active) learning process and annotation of the data, with indications of the progress of the trained model that can be downloaded and used in downstream tasks. Its architecture also allows the easy integration of other types of models, features and active learning strategies. The code is available on https://trusted-ai-labs.github/ALAMBIC/ and a video demonstration is available on https://youtu.be/4oh8UADfEmY.
AB - In this paper, we present ALAMBIC, an open-source dockerized web-based platform for annotating text data through active learning for classification tasks. Active learning is known to reduce the need of labelling, a time-consuming task, by selecting the most informative instances among the unlabelled instances, reaching an optimal accuracy faster than by just randomly labelling data. ALAMBIC integrates all the steps from data import to customization of the (active) learning process and annotation of the data, with indications of the progress of the trained model that can be downloaded and used in downstream tasks. Its architecture also allows the easy integration of other types of models, features and active learning strategies. The code is available on https://trusted-ai-labs.github/ALAMBIC/ and a video demonstration is available on https://youtu.be/4oh8UADfEmY.
UR - http://www.scopus.com/inward/record.url?scp=85159855604&partnerID=8YFLogxK
M3 - Conference contribution
AN - SCOPUS:85159855604
T3 - EACL 2023 - 17th Conference of the European Chapter of the Association for Computational Linguistics, Proceedings of System Demonstrations
SP - 117
EP - 127
BT - EACL 2023 - 17th Conference of the European Chapter of the Association for Computational Linguistics, Proceedings of System Demonstrations
PB - Association for Computational Linguistics (ACL)
Y2 - 2 May 2023 through 4 May 2023
ER -