Summary of the paper

Title ClearTK 2.0: Design Patterns for Machine Learning in UIMA
Authors Steven Bethard, Philip Ogren and Lee Becker
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.
Topics LR Infrastructures and Architectures, Information Extraction, Information Retrieval
Full paper ClearTK 2.0: Design Patterns for Machine Learning in UIMA
Bibtex @InProceedings{BETHARD14.218,
  author = {Steven Bethard and Philip Ogren and Lee Becker},
  title = {ClearTK 2.0: Design Patterns for Machine Learning in UIMA},
  booktitle = {Proceedings of the Ninth International Conference on Language Resources and Evaluation (LREC'14)},
  year = {2014},
  month = {may},
  date = {26-31},
  address = {Reykjavik, Iceland},
  editor = {Nicoletta Calzolari (Conference Chair) and Khalid Choukri and Thierry Declerck and Hrafn Loftsson and Bente Maegaard and Joseph Mariani and Asuncion Moreno and Jan Odijk and Stelios Piperidis},
  publisher = {European Language Resources Association (ELRA)},
  isbn = {978-2-9517408-8-4},
  language = {english}
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