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tmChem: a high performance approach for chemical named entity recognition and normalization
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<h3>tmChem: a high performance approach for chemical named entity recognition and normalization</h3>
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<h4>Authors: <a href="https://scholar.google.com/citations?user=FLnUx4cAAAAJ" target="_blank">Robert
Leaman</a>, <a href="https://sites.google.com/site/chihhsuanwei/" target="_blank">Chih-Hsuan Wei</a> and
<a href="/bionlp/" target="_blank">Zhiyong Lu</a> (PI)</h4>
<h4>Research highlights</h4>
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<p>
tmChem is an open-source software tool for identifying chemical names in biomedical literature,
including chemical identifiers, drug brand and trade names and also systematic formats. tmChem uses
conditional random fields with a rich feature set and rule-based post processing modules for
resolving local abbreviations and improving consistency. tmChem achieved the highest performance of
any submission to the BioCreative IV CHEMDNER task (over 87% F-measure).
</p>
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<h4>Method overview</h4>
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<p>
The tmChem system combines two linear chain conditional random fields (CRF) models employing
different tokenizations and feature sets. Model 1 is an adaptation of the BANNER named entity
recognizer. It uses the MALLET toolkit and is implemented in Java. Model 2 is repurposed from part
of the tmVar system for locating genetic variants. It uses the CRF++ toolkit and is implemented in
Perl and C++. Both models employ multiple post processing steps.
</p>
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<h4>Results</h4>
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tmChem was evaluated on the CHEMDNER test set, using the CEM task (named entity recognition) and
several strategies to combine the output of the two models.
</p>
<table class="customtable">
<tbody>
<tr>
<td align="center"><strong>Method</strong></td>
<td align="center"><strong>Precision</strong></td>
<td align="center"><strong>Recall</strong></td>
<td align="center"><strong>F-measure</strong></td>
</tr>
<tr>
<td align="center">Model 1</td>
<td align="center">0.8595</td>
<td align="center">0.8721</td>
<td align="center">0.8657</td>
</tr>
<tr>
<td align="center">Model 2</td>
<td align="center" class="best">0.8909</td>
<td align="center">0.8575</td>
<td align="center" class="best">0.8739</td>
</tr>
<tr>
<td align="center" nowrap="">Naive combination</td>
<td align="center">0.8192</td>
<td align="center">0.9209</td>
<td align="center">0.8671</td>
</tr>
<tr>
<td align="center" nowrap="">Heuristic combination</td>
<td align="center">0.8516</td>
<td align="center">0.8906</td>
<td align="center">0.8706</td>
</tr>
<tr>
<td align="center" nowrap="">High recall combination</td>
<td align="center">0.7672</td>
<td align="center" class="best">0.9212</td>
<td align="center">0.8372</td>
</tr>
</tbody>
</table>
<span><b>Table 1.</b> Evaluation of tmChem on the CHEMDNER test set using micro-averaged precsion, recall and F-measure.</span>
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<h4>Downloads</h4>
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<p>
<a href="/CBBresearch/Lu/Demo/tmTools/download/tmChem/tmChemM1-0.0.2.tgz"
target="_blank">Model 1 Source Code (Java)</a><br/>
<a href="/CBBresearch/Lu/Demo/tmTools/download/tmChem/tmChem.M2.ver02.zip"
target="_blank">Model 2 Source Code (Perl/C++)</a><br/>
tmChem-tagged PubMed results in <a href="/CBBresearch/Lu/Demo/PubTator/"
target="_blank">PubTator</a><br/>
<a href="/research/bionlp/APIs/">tmChem
RESTful API</a>
</p>
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<h4>Please cite</h4>
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<ul class="dot-list">
<li>Leaman R, Wei C-H, Lu Z. <a href="http://www.jcheminf.com/content/7/S1/S3/" target="_blank">tmChem:
a high performance tool for chemical named entity recognition and normalization</a>. Journal of
Cheminformatics, 7(Suppl 1):S3 (2015)
</li>
</ul>
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