ClearTK 2.0: Design patterns for machine learning in UIMA

Steven Bethard, Philip Ogren, Lee Becker

Research output: Chapter in Book/Report/Conference proceedingConference contribution

20 Scopus citations

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.

Original languageEnglish (US)
Title of host publicationProceedings of the 9th International Conference on Language Resources and Evaluation, LREC 2014
EditorsNicoletta Calzolari, Khalid Choukri, Sara Goggi, Thierry Declerck, Joseph Mariani, Bente Maegaard, Asuncion Moreno, Jan Odijk, Helene Mazo, Stelios Piperidis, Hrafn Loftsson
PublisherEuropean Language Resources Association (ELRA)
Pages3289-3293
Number of pages5
ISBN (Electronic)9782951740884
StatePublished - 2014
Externally publishedYes
Event9th International Conference on Language Resources and Evaluation, LREC 2014 - Reykjavik, Iceland
Duration: May 26 2014May 31 2014

Publication series

NameProceedings of the 9th International Conference on Language Resources and Evaluation, LREC 2014

Other

Other9th International Conference on Language Resources and Evaluation, LREC 2014
Country/TerritoryIceland
CityReykjavik
Period5/26/145/31/14

Keywords

  • Machine learning
  • NLP frameworks
  • UIMA

ASJC Scopus subject areas

  • Linguistics and Language
  • Library and Information Sciences
  • Education
  • Language and Linguistics

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