Dear Pete Whitelock,<br><br>we are actually organizing an named entity challenge related precisely to your question, the<br>CHEMDNER task of BioCreative IV (I have added the CFP below).<br><br>There are several approaches to used for the identification of drug names and chemical compounds,<br>
some based on dictionaries, other based on rules or machine learning methods. A review summarizing the<br>used methods is:<br><br>Vazquez, M., Krallinger, M., Leitner, F., & Valencia, A. (2011). Text Mining for Drugs and Chemical Compounds: Methods, Tools and Applications. Molecular Informatics, 30(6‐7), 506-519.<br>
<br>Regarding a dictionary for drug names, there are several databases that have lists of names (DrugBank,<br>CHEBI, PubChem,... ). A sort of more integrated resource is JOCHEM (<a href="http://www.biosemantics.org/index.php?page=Jochem">http://www.biosemantics.org/index.php?page=Jochem</a>)<br>
<br>Regards,<br><br><br>Martin Krallinger<br><br><br><br>--------------------------------------------<br><br>CALL FOR PARTICIPATION: CHEMDNER task: Chemical compound and drug name recognition task.<br><br>(<a href="http://www.biocreative.org/tasks/biocreative-iv/chemdner">http://www.biocreative.org/tasks/biocreative-iv/chemdner</a>) <br>
<br>TASK GOAL AND MOTIVATION<br>------------------------<br>The goal of this task is to promote the implementation of systems that are able to detect mentions in text of chemical compounds and drugs. The recognition of chemical entities is also crucial for other subsequent text processing strategies, such as detection of drug-protein interactions, adverse effects of chemical compounds or the extraction of pathway and metabolic reaction relations. A range of different methods have been explored for the<br>
recognition of chemical compound mentions including machine learning based approaches, rule-based systems and different types of dictionary-lookup strategies.<br><br>We foresee a considerable interest in the result of this task by the NLP/text mining community on one side, as well as by the bioinformatics, drug discovery/biomedicine and chemoinformatics communities on the other side. As has been the case in previous BioCreative efforts (resulting in high impact papers in the field), we expect that successful participants will have the opportunity to publish their system descriptions in a journal article.<br>
<br>CHEMDNER Track description.<br>------------------------------<br>The CHEMDNER is one of the tracks posed at the BioCreative IV community challenge (<a href="http://www.biocreative.org">http://www.biocreative.org</a>).<br>
<br>We invite participants to submit results for the CHEMDNER task providing predictions for one or both of the following subtasks:<br><br>a) Given a set of documents, return for each of them a ranked list of chemical entities described within each of these documents [Chemical document indexing sub-task]<br>
<br>b) Provide for a given document the start and end indices corresponding to all the chemical entities mentioned in this document [Chemical entity mention recognition sub-task]. <br><br>For these two tasks the organizers will release training and test data collections. The task organizers will provide details on the used annotation guidelines; define a list of criteria for relevant chemical compound entity types as well as selection of documents for annotation.<br>
<br>REGISTRATION<br>------------<br>Teams can participate in the CHEMDNER task by registering for track 2 of BioCreative IV. You can register additionally for other tracks too. To register your team go to the following page that provides more detailed instructions: <a href="http://www.biocreative.org/news/biocreative-iv/team/">http://www.biocreative.org/news/biocreative-iv/team/</a><br>
<br>Mailing list and contact information<br>You can post questions related to the CHEMDNER task to the BioCreative mailing list. To register for the BioCreative mailing list, please visit the following page: <a href="http://biocreative.sourceforge.net/mailing.html">http://biocreative.sourceforge.net/mailing.html</a><br>
<br>WORKSHOP<br>--------<br>CHEMDNER is part of the BioCreative evaluation effort. The BioCreative Organizing Committee will host the BioCreative IV Challenge evaluation workshop (<a href="http://www.biocreative.org/events/biocreative-iv/CFP/">http://www.biocreative.org/events/biocreative-iv/CFP/</a>) at NCBI, National Institutes of <br>
Health, Bethesda, Maryland, on October 7-9, 2013<br><br><br>CHEMDNER TASK ORGANIZERS<br>-------------------------<br>Martin Krallinger, Spanish National Cancer Research Center (CNIO)<br>Obdulia Rabal, University of Navarra, Spain<br>
Julen Oyarzabal, University of Navarra, Spain<br>Alfonso Valencia, Spanish National Cancer Research Center (CNIO)<br><br>REFERENCES<br>----------<br>- Vazquez, M., Krallinger, M., Leitner, F., & Valencia, A. (2011). Text Mining for Drugs and Chemical Compounds: Methods, Tools and Applications. Molecular Informatics, 30(6‐7), 506-519.<br>
- Corbett, P., Batchelor, C., & Teufel, S. (2007). Annotation of chemical named entities. BioNLP 2007: Biological, translational, and clinical language processing, 57-64.<br>- Klinger, R., Kolářik, C., Fluck, J., Hofmann-Apitius, M., & Friedrich, C. M. (2008). Detection of IUPAC and IUPAC-like chemical names. Bioinformatics, 24(13), i268-i276.<br>
- Hettne, K. M., Stierum, R. H., Schuemie, M. J., Hendriksen, P. J., Schijvenaars, B. J., Mulligen, E. M. V., ... & Kors, J. A. (2009). A dictionary to identify small molecules and drugs in free text. Bioinformatics, 25(22), 2983-2991.<br>
- Yeh, A., Morgan, A., Colosimo, M., & Hirschman, L. (2005). BioCreAtIvE task 1A: gene mention finding evaluation. BMC bioinformatics, 6(Suppl 1), S2.<br>- Smith, L., Tanabe, L. K., Ando, R. J., Kuo, C. J., Chung, I. F., Hsu, C. N., ... & Wilbur, W. J. (2008). Overview of BioCreative II gene mention recognition. Genome Biology, 9(Suppl 2), S2.<br>
<br><br><br><div class="gmail_quote">On Tue, Jan 8, 2013 at 4:45 PM, WHITELOCK, Pete <span dir="ltr"><<a href="mailto:pete.whitelock@oup.com" target="_blank">pete.whitelock@oup.com</a>></span> wrote:<br><blockquote class="gmail_quote" style="margin:0 0 0 .8ex;border-left:1px #ccc solid;padding-left:1ex">
<div link="blue" vlink="purple" lang="EN-GB"><div><p class="MsoNormal">I’m interested in the problem of spotting that a particular string that’s not in one’s dictionary is in fact the name of a drug. New drugs and their names are being created all the time and it’s pretty easy as a human to see a string in isolation and see “yeh, that’s a drug name”. Anyone done anything similar to this? I vaguely recall some discussion of distinguishing boys’ and girls’ names (as an exercise in some textbook?).<u></u><u></u></p>
<p class="MsoNormal"><u></u> <u></u></p><p class="MsoNormal">In addition, does anyone know where to get a list of drug names to use as the starting point. <u></u><u></u></p><p class="MsoNormal"><u></u> <u></u></p><p class="MsoNormal">
Thanks for any help<u></u><u></u></p><p class="MsoNormal"><u></u> <u></u></p><p class="MsoNormal"><span style="font-size:10.0pt;font-family:"Courier New"">Pete Whitelock, PhD</span><span><br></span><span style="font-size:10.0pt;font-family:"Courier New"">Principal Language Engineer, Technology<u></u><u></u></span></p>
<p class="MsoNormal"><span style="font-size:10.0pt;font-family:"Courier New"">Academic Dictionaries</span><span> <br></span><span style="font-size:10.0pt;font-family:"Courier New"">Oxford University Press</span><span><u></u><u></u></span></p>
<p class="MsoNormal"><u></u> <u></u></p></div>
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