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<div>Claudiu Mihaila, Tomoko Ohta, Sampo Pyysalo and Sophia Ananiadou<span class="Apple-tab-span" style="white-space: pre; ">
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<div>"BioCause: Annotating and analysing causality in the biomedical domain"</div>
<div>BMC Bioinformatics 2013, 14:2 </div>
<div><a href="http://www.biomedcentral.com/1471-2105/14/2/">http://www.biomedcentral.com/1471-2105/14/2/</a></div>
<div>doi:10.1186/1471-2105-14-2</div>
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<div>Abstract</div>
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<div>Background</div>
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<div>Biomedical corpora annotated with event-level information represent an important resource for domain-specific information extraction (IE) systems. However, bio-event annotation alone cannot cater for all the needs of biologists. Unlike work on relation
and event extraction, most of which focusses on specific events and named entities, we aim to build a comprehensive resource, covering all statements of causal association present in discourse. Causality lies at the heart of biomedical knowledge, such as diagnosis,
pathology or systems biology, and, thus, automatic causality recognition can greatly reduce the human workload by suggesting possible causal connections and aiding in the curation of pathway models. A biomedical text corpus annotated with such relations is,
hence, crucial for developing and evaluating biomedical text mining. </div>
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<div>Results</div>
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<div>We have defined an annotation scheme for enriching biomedical domain corpora with causality relations. This schema has subsequently been used to annotate 851 causal relations to form BioCause, a collection of 19 open-access full-text biomedical journal
articles belonging to the subdomain of infectious diseases. These documents have been pre-annotated with named entity and event information in the context of previous shared tasks. We report an inter-annotator agreement rate of over 60% for triggers and of
over 80% for arguments using an exact match constraint. These increase significantly using a relaxed match setting. Moreover, we analyse and describe the causality relations in BioCause from various points of view. This information can then be leveraged for
the training of automatic causality detection systems. </div>
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<div>Conclusion</div>
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<div>Augmenting named entity and event annotations with information about causal discourse relations could benefit the development of more sophisticated IE systems. These will further influence the development of multiple tasks, such as enabling textual inference
to detect entailments, discovering new facts and providing new hypotheses for experimental work. </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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<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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