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Related Concept Videos

Chemical Shift: Internal References and Solvent Effects01:17

Chemical Shift: Internal References and Solvent Effects

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Related Experiment Videos

Using workflows to explore and optimise named entity recognition for chemistry.

Balakrishna Kolluru1, Lezan Hawizy, Peter Murray-Rust

  • 1National Centre for Text Mining, Manchester Interdisciplinary Biocentre, University of Manchester, Manchester, United Kingdom. balakrishna.kolluru@manchester.ac.uk

Plos One
|June 3, 2011
PubMed
Summary
This summary is machine-generated.

Reconfigurable workflows improve chemistry text mining by enhancing named entity recognition (NER) accuracy. Optimizing tokenization, a crucial step, significantly boosts performance in identifying chemical names.

Related Experiment Videos

Area of Science:

  • Computational chemistry
  • Bioinformatics
  • Natural Language Processing

Background:

  • Chemistry text mining tools require interoperability and adaptability.
  • Existing tools often face system-level implementation, installation, or programming challenges.
  • Abstracting tool functionality from underlying implementations is crucial for broader usability.

Purpose of the Study:

  • To abstract the functionality of chemistry text mining tools from their implementation.
  • To develop reconfigurable workflows for automatic chemical name identification.
  • To study the impact of individual components on the performance of named entity recognition (NER).

Main Methods:

  • Refactoring the OSCAR named entity recognizer for the chemistry domain.
  • Developing two reconfigurable workflows using the U-Compare interoperable text mining framework.
  • Investigating the relationship between tokenization and NER using Maximum Entropy Markov Model (MEMM) and pattern recognition classifiers.

Main Results:

  • Eliminating noise from tokenization techniques slightly improves NER accuracy in chemistry.
  • Poor tokenization negatively impacts classifier performance, increasing errors.
  • The workflow-based system using a new tokenizer increased the F-score from 82.35% to 84.44% on the Sciborg corpus.
  • On the PubMed corpus, the workflow system achieved an F-score of 84.84% compared to OSCAR's 84.23%.

Conclusions:

  • Reconfigurable workflows enhance the performance of chemistry text mining tools.
  • Optimizing tokenization is critical for improving NER accuracy.
  • The U-Compare framework facilitates the development and study of adaptable text mining workflows.