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<div>[Apologies for cross-postings]</div>
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<div>Wide coverage biomedical event extraction using multiple partially overlapping corpora</div>
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<div>Makoto Miwa, Sampo Pyysalo, Tomoko Ohta and Sophia Ananiadou</div>
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<div>BMC Bioinformatics 2013, 14:175</div>
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<div><a href="http://www.biomedcentral.com/1471-2105/14/175">http://www.biomedcentral.com/1471-2105/14/175</a></div>
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<div>Abstract</div>
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<div>Background</div>
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<div>Biomedical events are key to understanding physiological processes and disease, and wide coverage extraction is required for comprehensive automatic analysis of statements describing biomedical systems in the literature. In turn, the training and evaluation
of extraction methods requires manually annotated corpora. However, as manual annotation is time-consuming and expensive, any single event-annotated corpus can only cover a limited number of semantic types. Although combined use of several such corpora could
potentially allow an extraction system to achieve broad semantic coverage, there has been little research into learning from multiple corpora with partially overlapping semantic annotation scopes.</div>
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<div>Results</div>
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<div>We propose a method for learning from multiple corpora with partial semantic annotation overlap, and implement this method to improve our existing event extraction system, EventMine. An evaluation using seven event annotated corpora, including 65 event
types in total, shows that learning from overlapping corpora can produce a single, corpus-independent, wide coverage extraction system that outperforms systems trained on single corpora and exceeds previously reported results on two established event extraction
tasks from the BioNLP Shared Task 2011.</div>
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<div>Conclusions</div>
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<div>The proposed method allows the training of a wide-coverage, state-of-the-art event extraction system from multiple corpora with partial semantic annotation overlap. The resulting single model makes broad-coverage extraction straightforward in practice
by removing the need to either select a subset of compatible corpora or semantic types, or to merge results from several models trained on different individual corpora. Multi-corpus learning also allows annotation efforts to focus on covering additional semantic
types, rather than aiming for exhaustive coverage in any single annotation effort, or extending the coverage of semantic types annotated in existing corpora.</div>
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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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