Biomedical Abbreviation Recognition and Resolution (BARR) track

BARR track - IberEval 2017

Biomedical Abbreviation Recognition and Resolution (BARR) track

September 19th, 2017: Workshop at SEPLN 2017

http://temu.inab.org/index.html

Overview
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The recognition and resolution of abbreviations, acronyms and symbols is a
critical step for a number of tasks including named entity recognition
(NER), machine translation, information retrieval/indexing and document
categorization among others. Therefore the implementation and availability
of abbreviation recognition systems is of great practical impact for text
mining and language processing.

In case of domains such as biomedicine and clinical research, abbreviations
are particularly frequent, often referring to entities and concepts of
importance such as genes, diseases, symptoms, drugs/chemicals or
treatments. NER, relation extraction and clinical document coding systems
usually need to cope with recognizing correctly short forms or
abbreviations.

Abbreviations can be regarded as a ShortForm (SF) that denotes a longer word
or phrase (LongForm, LF), typically its definition. Different strategies
have been tested to detect short forms in English biomedical texts (Torii et
al., 2007), using for instance alignment-based approaches, machine learning
methods or rule-based strategies and some manually annotated corpora do
exist (e.g. MEDSTRACT, Ab3P or BIOADI, see Islamaj Doğan et al., 2014). Far
less effort has been made to detect short form- long form pairs in text
written in other languages.

There is a growing number of biomedical and clinical documents written in
Spanish, such as medical literature, medical agency reports, patents and
particularly electronic health records. Moreover, according to some
estimates there are over 500 million Spanish speakers worldwide.

As part of the IBEREVAL 2017
(http://cabrillo.lsi.uned.es/nlp/IberEval-2017/index.php) initiative we have
proposed the Biomedical Abbreviation Recognition and Resolution (BARR) track
with the aim promoting the development and evaluation of biomedical
abbreviation identification systems.

For this track, participating teams have to detect mentions of pairs of
Short Forms and their corresponding Long Forms from medical article
abstracts written in Spanish. BARR track organizers provide a manually
labeled training set exhaustively tagged with Short Form-Long Form pairs (in
addition to other abbreviations).

This track is particularly interesting as some abstracts were manually
transcribed, resembling preprocessing characteristics also found in clinical
documents. In addition to the BARR manually annotated Gold Standard corpora,
the BARR document collection will be released, consisting in the largest
existing unified collection of medical article abstracts written in Spanish
distributed through a special agreement with the publisher Elsevier to
participating teams.

Additional details, sample sets, FAQ and inscription details can be found at:

BARR track URL: http://temu.inab.org/index.html

Contact e-mail: Martin Krallinger , mkrallinger [at] cnio.es

Important tentative dates
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March 28th, 2017: Release of sample data
May 19th, 2017: Release of training data subset1
May 30th, 2017: Release of training data (full set)
June 19th, 2017: Release of Testing Set
June 24th, 2017: Submission of participant runs
June 26th, 2017: Working notes submission due (short system description 3-5 pages)
June 28th, 2017: Reviews of Working notes sent out to authors
July 1st, 2017: Deadline to submit Camera ready revised Working notes
September 19th, 2017: Workshop at SEPLN 2017

BARR track organizers
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Martin Krallinger, Biological Text Mining Unit (Bio-TeMUC), CNIO, Spain
Santiago de la Peña, Biological Text Mining Unit (Bio-TeMUC), CNIO, Spain
Ander Intxaurrondo, Biological Text Mining Unit (Bio-TeMUC), CNIO, Spain
Jesús Santamaría, Biological Text Mining Unit (Bio-TeMUC), CNIO, Spain
Jose A. Lopez-Martin, Medical Oncology, Hospital 12 de Octubre, Spain
Alfonso Valencia, Life Sciences & Computational Biology, BSC, Spain
Marta Villegas, Life Sciences & Computational Genomics, BSC, Spain
Anália Lourenço, Next Generation Computer Systems Group, University of Vigo, Spain
Gael Pérez, Next Generation Computer Systems Group, University of Vigo, Spain
Martín Pérez, Next Generation Computer Systems Group, University of Vigo, Spain