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Raheel Nawaz, Paul Thompson and Sophia Ananiadou</div>
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<p class="authors">"Negated bio-events: analysis and identification"</p>
<p class="authors">BMC Bioinformatics 2013, 14:14 </p>
<p class="authors"><a href="http://www.biomedcentral.com/1471-2105/14/14/">http://www.biomedcentral.com/1471-2105/14/14/</a></p>
<p class="authors">doi:10.1186/1471-2105-14-14</p>
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<p class="authors">Abstract</p>
<p class="authors">========</p>
<p class="authors">Background</p>
<p class="authors">----------------</p>
<p class="authors">Negation occurs frequently in scientific literature, especially in biomedical literature. It has previously been reported that around 13% of sentences found in biomedical research articles contain negation. Historically, the main motivation
for identifying negated events has been to ensure their exclusion from lists of extracted interactions. However, recently, there has been a growing interest in negative results, which has resulted in negation detection being identified as a key challenge in
biomedical relation extraction. In this article, we focus on the problem of identifying negated bio-events, given gold standard event annotations. </p>
<p class="authors">Results</p>
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<p class="authors">We have conducted a detailed analysis of three open access bio-event corpora containing negation information (i.e., GENIA Event, BioInfer and BioNLP'09 ST), and have identified the main types of negated bio-events. We have analysed the key
aspects of a machine learning solution to the problem of detecting negated events, including selection of negation cues, feature engineering and the choice of learning algorithm. Combining the best solutions for each aspect of the problem, we propose a novel
framework for the identification of negated bio-events. We have evaluated our system on each of the three open access corpora mentioned above. The performance of the system significantly surpasses the best results previously reported on the BioNLP'09 ST corpus,
and achieves even better results on the GENIA Event and BioInfer corpora, both of which contain more varied and complex events. </p>
<p class="authors">Conclusion</p>
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<p class="authors">Recently, in the field of biomedical text mining, the development and enhancement of event-based systems has received significant interest. The ability to identify negated events is a key performance element for these systems. We have conducted
the first detailed study on the analysis and identification of negated bio-events. Our proposed framework can be integrated with state-of-the-art event extraction systems. The resulting systems will be able to extract bio-events with attached polarities from
textual documents, which can serve as the foundation for more elaborate systems that are able to detect mutually contradicting bio-events. </p>
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<div>Paul Thompson<br>
Research Associate<br>
School of Computer Science<br>
National Centre for Text Mining<br>
Manchester Institute of Biotechnology<br>
University of Manchester<br>
131 Princess Street<br>
Manchester<br>
M1 7DN<br>
UK<br>
Tel: 0161 306 3091<br>
<a href="http://personalpages.manchester.ac.uk/staff/Paul.Thompson/">http://personalpages.manchester.ac.uk/staff/Paul.Thompson/</a></div>
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