Exploring two biomedical text genres for disease recognition

Authors:
Aurélie Névéol;Won Kim;W. John Wilbur;Zhiyong Lu
Affiliations:
National Center for Biotechnology Information, Bethesda, MD;National Center for Biotechnology Information, Bethesda, MD;National Center for Biotechnology Information, Bethesda, MD;National Center for Biotechnology Information, Bethesda, MD
Venue:
BioNLP '09 Proceedings of the Workshop on Current Trends in Biomedical Natural Language Processing
Year:
2009

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Abstract

In the framework of contextual information retrieval in the biomedical domain, this paper reports on the automatic detection of disease concepts in two genres of biomedical text: sentences from the literature and PubMed user queries. A statistical model and a Natural Language Processing algorithm for disease recognition were applied on both corpora. While both methods show good performance (F=77% vs. F=76%) on the sentence corpus, results on the query corpus indicate that the statistical model is more robust (F=74% vs. F=70%).