TextPro-AL: An Active Learning Platform for Flexible and Efficient Production of Training Data for NLP Tasks

Bernardo Magnini, Anne-Lyse Minard, Mohammed R. H. Qwaider, Manuela Speranza


Abstract
This paper presents TextPro-AL (Active Learning for Text Processing), a platform where human annotators can efficiently work to produce high quality training data for new domains and new languages exploiting Active Learning methodologies. TextPro-AL is a web-based application integrating four components: a machine learning based NLP pipeline, an annotation editor for task definition and text annotations, an incremental re-training procedure based on active learning selection from a large pool of unannotated data, and a graphical visualization of the learning status of the system.
Anthology ID:
C16-2028
Volume:
Proceedings of COLING 2016, the 26th International Conference on Computational Linguistics: System Demonstrations
Month:
December
Year:
2016
Address:
Osaka, Japan
Editor:
Hideo Watanabe
Venue:
COLING
SIG:
Publisher:
The COLING 2016 Organizing Committee
Note:
Pages:
131–135
Language:
URL:
https://aclanthology.org/C16-2028
DOI:
Bibkey:
Cite (ACL):
Bernardo Magnini, Anne-Lyse Minard, Mohammed R. H. Qwaider, and Manuela Speranza. 2016. TextPro-AL: An Active Learning Platform for Flexible and Efficient Production of Training Data for NLP Tasks. In Proceedings of COLING 2016, the 26th International Conference on Computational Linguistics: System Demonstrations, pages 131–135, Osaka, Japan. The COLING 2016 Organizing Committee.
Cite (Informal):
TextPro-AL: An Active Learning Platform for Flexible and Efficient Production of Training Data for NLP Tasks (Magnini et al., COLING 2016)
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PDF:
https://aclanthology.org/C16-2028.pdf