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ClearTK: A Framework for Statistical Biomedical Natural Language Processing Philip Ogren Philipp Wetzler Department of Computer Science University of Colorado.

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Presentation on theme: "ClearTK: A Framework for Statistical Biomedical Natural Language Processing Philip Ogren Philipp Wetzler Department of Computer Science University of Colorado."— Presentation transcript:

1 ClearTK: A Framework for Statistical Biomedical Natural Language Processing Philip Ogren Philipp Wetzler Department of Computer Science University of Colorado at Boulder

2 Introduction ClearTK is a software package that: – facilitates statistical biomedical natural language processing – is written for UIMA Java – Provides extensible feature extraction library – Interfaces with popular machine learning libraries Maximum Entropy (OpenNLP) Support Vector Machines (LIBSVM) Conditional Random Fields (Mallet) Misc. –e.g. Naïve Bayes (Weka) Available free for academic research (contact

3 UIMA 101 text Common Analysis Structure (CAS) collection reader analysis engines consumers ClearTK provides a way to create analysis engines that use statistical models for classifying text. The structure of the CAS is defined by a type system determined by the development team.

4 Statistical Biomedical Natural Language Processing 101 Frame NLP task as classification task – e.g. For named entity recognition classify tokens as one of “B”, “I”, or “O”. Training – Manually annotate a bunch of data – Extract features from text * – Write out training data * – Train a model Run time – Extract features from unseen text * – Classify features with trained model* – Create annotations * ClearTK facilitates these tasks The concentration of alpha 2-macroglobulin, alpha 1- antitrypsin, plasminogen, C3-complement, fibrinogen degradation products (FDP) and fibrinolytic activity...

5 ClearTK Analysis Engine UIMA CAS input annotations extract features feature set classify UIMA CAS output annotations find foci of analysis interpret result / create annotations training data

6 ClearTK Summary Provides a framework that simplifies feature extraction and interfacing with a wide variety of machine learning libraries. Is not dependent on any specific type system Provides sophisticated feature extractors. Provides infrastructure supporting core library (i.e. collection readers, analysis engines, consumers, etc.) Well documented and unit tested.


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