Challenges for extracting biomedical knowledge from full text

Authors:
Tara McIntosh;James R. Curran
Affiliations:
University of Sydney, Australia;University of Sydney, Australia
Venue:
BioNLP '07 Proceedings of the Workshop on BioNLP 2007: Biological, Translational, and Clinical Language Processing
Year:
2007

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Abstract

At present, most biomedical Information Retrieval and Extraction tools process abstracts rather than full-text articles. The increasing availability of full text will allow more knowledge to be extracted with greater reliability. To investigate the challenges of full-text processing, we manually annotated a corpus of cited articles from a Molecular Interaction Map (Kohn, 1999). Our analysis demonstrates the necessity of full-text processing; identifies the article sections where interactions are most commonly stated; and quantifies both the amount of external knowledge required and the proportion of interactions requiring multiple or deeper inference steps. Further, it identifies a range of NLP tools required, including: identifying synonyms, and resolving coreference and negated expressions. This is important guidance for researchers engineering biomedical text processing systems.