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ClearTK: A Framework for Statistical Biomedical Natural Language Processing

ClearTK: A Framework for Statistical Biomedical Natural Language Processing. Philip Ogren Philipp Wetzler. Department of Computer Science University of Colorado at Boulder. Introduction. ClearTK is a software package that: f acilitates statistical biomedical natural language processing

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ClearTK: A Framework for Statistical Biomedical Natural Language Processing

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  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 philip@ogren.info)

  3. UIMA 101 Common Analysis Structure (CAS) analysis engines text collection reader • 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. consumers

  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... O O O B I B I B B B I I O O O

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