Literature DB >> 29104966

ClearTK 2.0: Design Patterns for Machine Learning in UIMA.

Steven Bethard1, Philip Ogren2, Lee Becker2.   

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.

Entities:  

Keywords:  NLP frameworks; UIMA; machine learning

Year:  2014        PMID: 29104966      PMCID: PMC5667672     

Source DB:  PubMed          Journal:  LREC Int Conf Lang Resour Eval


  1 in total

1.  Discovering body site and severity modifiers in clinical texts.

Authors:  Dmitriy Dligach; Steven Bethard; Lee Becker; Timothy Miller; Guergana K Savova
Journal:  J Am Med Inform Assoc       Date:  2013-10-03       Impact factor: 4.497

  1 in total
  2 in total

1.  Risk factor detection for heart disease by applying text analytics in electronic medical records.

Authors:  Manabu Torii; Jung-Wei Fan; Wei-Li Yang; Theodore Lee; Matthew T Wiley; Daniel S Zisook; Yang Huang
Journal:  J Biomed Inform       Date:  2015-08-14       Impact factor: 6.317

2.  Identifying direct temporal relations between time and events from clinical notes.

Authors:  Hee-Jin Lee; Yaoyun Zhang; Min Jiang; Jun Xu; Cui Tao; Hua Xu
Journal:  BMC Med Inform Decis Mak       Date:  2018-07-23       Impact factor: 2.796

  2 in total

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