Abstract
ClearTK adds machine learning functionality to the UIMA framework, providing wrappers to popular machine learning libraries, a rich feature extraction library that works across different classifiers, and utilities for applying and evaluating machine learning models. Since its inception in 2008, ClearTK has evolved in response to feedback from developers and the community. This evolution has followed a number of important design principles including: conceptually simple annotator interfaces, readable pipeline descriptions, minimal collection readers, type system agnostic code, modules organized for ease of import, and assisting user comprehension of the complex UIMA framework.- Anthology ID:
- L14-1213
- Volume:
- Proceedings of the Ninth International Conference on Language Resources and Evaluation (LREC'14)
- Month:
- May
- Year:
- 2014
- Address:
- Reykjavik, Iceland
- Editors:
- Nicoletta Calzolari, Khalid Choukri, Thierry Declerck, Hrafn Loftsson, Bente Maegaard, Joseph Mariani, Asuncion Moreno, Jan Odijk, Stelios Piperidis
- Venue:
- LREC
- SIG:
- Publisher:
- European Language Resources Association (ELRA)
- Note:
- Pages:
- 3289–3293
- Language:
- URL:
- http://www.lrec-conf.org/proceedings/lrec2014/pdf/218_Paper.pdf
- DOI:
- Cite (ACL):
- Steven Bethard, Philip Ogren, and Lee Becker. 2014. ClearTK 2.0: Design Patterns for Machine Learning in UIMA. In Proceedings of the Ninth International Conference on Language Resources and Evaluation (LREC'14), pages 3289–3293, Reykjavik, Iceland. European Language Resources Association (ELRA).
- Cite (Informal):
- ClearTK 2.0: Design Patterns for Machine Learning in UIMA (Bethard et al., LREC 2014)
- PDF:
- http://www.lrec-conf.org/proceedings/lrec2014/pdf/218_Paper.pdf