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NOBLE - Flexible concept recognition for large-scale biomedical natural language processing.

Eugene Tseytlin1, Kevin Mitchell2, Elizabeth Legowski3

  • 1Department of Biomedical Informatics, University of Pittsburgh School of Medicine, The Offices at Baum, 5607 Baum Boulevard, BAUM 423, Rm 523, Pittsburgh, PA, 15206-3701, USA. eugene.tseytlin@gmail.com.

BMC Bioinformatics
|January 15, 2016
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Summary
This summary is machine-generated.

NOBLE Coder is a new, flexible tool for concept recognition in biomedical natural language processing (NLP). It offers comparable accuracy and speed to existing systems, with unique advantages in configuration and adaptability for diverse NLP tasks.

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Area of Science:

  • Biomedical Informatics
  • Computational Linguistics

Background:

  • Natural Language Processing (NLP) is crucial for biomedical data analysis.
  • Concept recognition is a key NLP task, with both general and domain-specific applications.
  • Existing tools often lack flexibility or are domain-specific.

Purpose of the Study:

  • Introduce NOBLE Coder, a novel, general-purpose concept recognition component for NLP pipelines.
  • Evaluate NOBLE Coder's speed and accuracy against established alternatives.
  • Highlight NOBLE Coder's unique features and adaptability.

Main Methods:

  • Developed NOBLE Coder with a general algorithm for term-to-concept matching.
  • Configured matching options for specific NLP task behaviors.
  • Benchmarked speed and accuracy on biological (CRAFT) and clinical (ShARe) corpora.
  • Compared performance against MMTx, MGrep, Concept Mapper, and cTAKES DLAs.

Main Results:

  • NOBLE Coder demonstrated competitive speed and accuracy, outperforming common alternatives.
  • Its performance was comparable to state-of-the-art systems.
  • Error analysis indicated distinct error profiles among the evaluated systems.
  • Key advantages include a greedy algorithm, configurable matching, and multiple input formats.

Conclusions:

  • NOBLE Coder is a viable option for general concept recognition in biomedical NLP.
  • It offers comparable accuracy and speed to existing systems.
  • Advantages include an interactive terminology builder, ease of configuration, and adaptability to various domains and tasks.