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<div><span class="Apple-style-span" style="font-family: Arial; ">Anatomical entity mention recognition at literature scale</span></div>
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<div apple-content-edited="true"><span class="Apple-style-span" style="font-family: Arial; ">Sampo Pyysalo and Sophia Ananiadou</span></div>
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<div apple-content-edited="true"><span class="Apple-style-span" style="font-family: Arial; ">Bioinformatics 2013, doi: 10.1093/bioinformatics/btt580</span></div>
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<div apple-content-edited="true"><span class="Apple-style-span" style="font-family: Arial; "><a href="http://bioinformatics.oxfordjournals.org/content/early/2013/10/24/bioinformatics.btt580.abstract">http://bioinformatics.oxfordjournals.org/content/early/2013/10/24/bioinformatics.btt580.abstract</a></span></div>
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<div apple-content-edited="true"><span class="Apple-style-span" style="font-family: Arial; ">Abstract</span></div>
<div apple-content-edited="true"><span class="Apple-style-span" style="font-family: Arial; ">=======</span></div>
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<div apple-content-edited="true"><span class="Apple-style-span" style="font-family: Arial; ">Motivation</span></div>
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<div apple-content-edited="true"><span class="Apple-style-span" style="font-family: Arial; ">Anatomical entities ranging from sub-cellular structures to organ systems are central to biomedical science, and mentions of these entities are essential to understanding
the scientific literature. Despite extensive efforts to automatically analyse various aspects of biomedical text, there have been only few studies focusing on anatomical entities, and no dedicated methods for learning to automatically recognize anatomical
entity mentions in free-form text have been introduced.</span></div>
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<div apple-content-edited="true"><span class="Apple-style-span" style="font-family: Arial; ">Results</span></div>
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<div apple-content-edited="true"><span class="Apple-style-span" style="font-family: Arial; ">We present AnatomyTagger, a machine learning-based system for anatomical entity mention recognition. The system incorporates a broad array of approaches proposed to
benefit tagging, including the use of UMLS- and OBO-based lexical resources, word representations induced from unlabelled text, statistical truecasing, and non-local features. We train and evaluate the system on a newly introduced corpus that substantially
extends on previously available resources, and apply the resulting tagger to automatically annotate the entire Open Access scientific domain literature. The resulting analyses have been applied to extend services provided by the Europe PMC literature database.</span></div>
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<div apple-content-edited="true"><span class="Apple-style-span" style="font-family: Arial; ">Availability</span></div>
<div apple-content-edited="true"><span class="Apple-style-span" style="font-family: Arial; ">--------------</span></div>
<div apple-content-edited="true"><span class="Apple-style-span" style="font-family: Arial; ">All tools and resources introduced in this work are available from
<a href="http://nactem.ac.uk/anatomytagger">http://nactem.ac.uk/anatomytagger</a></span></div>
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<div apple-content-edited="true"><span class="Apple-style-span" style="font-family: Arial; ">Resources</span></div>
<div apple-content-edited="true"><span class="Apple-style-span" style="font-family: Arial; ">=========</span><span class="Apple-style-span" style="orphans: 2; text-align: -webkit-auto; text-indent: 0px; widows: 2; -webkit-text-decorations-in-effect: none; "><span class="Apple-style-span" style="orphans: 2; text-align: -webkit-auto; text-indent: 0px; widows: 2; -webkit-text-decorations-in-effect: none; "><span class="Apple-style-span" style="orphans: 2; text-align: -webkit-auto; text-indent: 0px; widows: 2; -webkit-text-decorations-in-effect: none; ">
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<h1 id="article-title-1" itemprop="headline"><font class="Apple-style-span" size="3"><span class="Apple-style-span" style="font-weight: normal;">The following resources described in the paper have all been made available under open source (MIT) and open data
(CC BY-SA) licences:</span></font></h1>
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</span></span></span><span class="Apple-style-span" style="font-family: Arial; ">- AnatEM: corpus of 1200 documents manually annotated for 13,700 anatomical entity mentions</span></div>
<div apple-content-edited="true"><span class="Apple-style-span" style="font-family: Arial; ">- AnatomyTagger: tool for the recognition of anatomical entity mentions in text</span></div>
<div apple-content-edited="true"><span class="Apple-style-span" style="font-family: Arial; ">- Results of tagging all of the 600,000 PMC OA full-text documents, identifying 48M anatomical entity mentions</span></div>
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<div apple-content-edited="true"><span class="Apple-style-span" style="font-family: Arial; ">For these and other resources, please see the AnatomyTagger homepage:</span></div>
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<div apple-content-edited="true"><span class="Apple-style-span" style="font-family: Arial; "><a href="http://nactem.ac.uk/anatomytagger/">http://nactem.ac.uk/anatomytagger/</a></span></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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