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BioC: a minimalist approach to interoperability for biomedical text processing.

Donald C Comeau1, Rezarta Islamaj Doğan, Paolo Ciccarese

  • 1National Center for Biotechnology Information, National Library of Medicine, Bethesda, MD 20894, USA, Department of Neurology, Massachusetts General Hospital, Boston, MA 02114, Harvard Medical School, Harvard University, Boston, MA 02115 USA, Center for Computational Pharmacology, University of Colorado Denver School of Medicine, Aurora, CO 80045, USA, Structural and Computational Biology Group, Spanish National Cancer Research Centre, Madrid E-28029, Spain, Center for Bioinformatics and Computational Biology, Department of Computer and Information Sciences, University of Delaware, Newark, DE 19711, USA, Institute of Computational Linguistics, University of Zurich, Zurich 8050, Switzerland, National ICT Australia (NICTA), Victoria Research Laboratory, The University of Melbourne, Parkville VIC 3010, Australia and Department of Biology, North Carolina State University, Raleigh, NC 27695, USA.

Database : the Journal of Biological Databases and Curation
|September 20, 2013
PubMed
Summary
This summary is machine-generated.

This study introduces a new extensible mark-up language format for sharing scientific text and annotations. This format facilitates data exchange between natural language processing and text mining tools, improving information extraction.

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Area of Science:

  • Bioinformatics
  • Computational Biology
  • Natural Language Processing

Background:

  • Scientific literature is rapidly expanding, making manual information extraction infeasible.
  • Natural language processing (NLP) and text mining are crucial for accessing scientific data.
  • A standardized format is needed for seamless data exchange between NLP and text mining systems.

Purpose of the Study:

  • To propose a simple, extensible mark-up language (XML) format for sharing scientific text and annotations.
  • To enable efficient data representation, storage, and exchange between diverse language processing tools.
  • To support various annotation types, including entities and relationships.

Main Methods:

  • Development of a novel extensible mark-up language (XML) format for scientific text and annotations.
  • Implementation of code for reading, writing, and processing data in the proposed XML format.
  • Demonstration of the format's capability to represent diverse annotations like sentences, tokens, parts of speech, named entities (genes, diseases), and relationships.

Main Results:

  • A simple and extensible XML format for representing scientific documents and annotations has been developed.
  • The format supports a wide range of annotations, including complex biological entities and their relationships.
  • Associated code for data handling and processing is provided, facilitating practical application.

Conclusions:

  • The proposed XML format offers a standardized solution for interchanging annotated scientific text.
  • This approach enhances the integration and capabilities of natural language processing and text mining tools.
  • The developed format and tools aim to streamline information extraction and knowledge discovery from scientific literature.