Conference paper Open Access

Automatic detection of inadequate claims in biomedical articles: first steps

Koroleva Anna; Paroubek Patrick

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<oai_dc:dc xmlns:dc="" xmlns:oai_dc="" xmlns:xsi="" xsi:schemaLocation="">
  <dc:creator>Koroleva Anna</dc:creator>
  <dc:creator>Paroubek Patrick</dc:creator>
  <dc:description>In this article we present the first steps in developing an NLP algorithm for automatic detection of inadequate reporting of research results (known as spin) in biomedical articles. Inadequate reporting consists in presenting the experimental treatment as having a greater beneficial effect than it was shown by the research results. We propose a scheme for an algorithm that would automatically identify important claims in the articles abstracts, extract possible
supporting information from the article and check the adequacy of the claims. We present the state of the art and our first experiments for three tasks related to spin detection: classification of articles according to the type of reported clinical trial; classification of sentences in the abstracts aimed at identifying mentions of the Results and Conclusions of the experiment; and extraction of some trial characteristics. For each task, we outline possible directions of  further work.</dc:description>
  <dc:subject>Inadequate Reporting</dc:subject>
  <dc:subject>Biomedical Articles</dc:subject>
  <dc:subject>Text Classification</dc:subject>
  <dc:subject>Entity Extraction</dc:subject>
  <dc:title>Automatic detection of inadequate claims in biomedical articles: first steps</dc:title>
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