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:
- 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)
- PDF:
- https://preview.aclanthology.org/ingest-acl-2023-videos/C16-2028.pdf