First Challenge Task: Drug-Drug Interaction Extraction. http://labda.inf.uc3m.es/DDIExtraction2011/

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A Drug-Drug Interaction (DDI) occurs when one drug influences the
level or activity of another, for example, raising its blood drug
levels and possibly intensifying its side effects or decreasing drug
concentrations and thereby reducing its effectiveness. The detection
of DDI is an important research area in patient safety since these
interactions can become very dangerous and increase health care costs.
Although there are different databases supporting health care
professionals in the detection of DDI, these databases are rarely
complete, since their update periods can reach three years. Drug
interactions are frequently reported in journals of clinical
pharmacology and technical reports, making medical literature the most
effective source for the detection of DDI. Thus, the management of DDI
is a critical issue due to the overwhelming amount of information
available on them.

Information Extraction (IE) can be of great benefit in the
pharmaceutical industry allowing identification and extraction of
relevant information on DDI and providing an interesting way of
reducing the time spent by health care professionals on reviewing the
literature. Moreover, the development of tools for automatically
extracting DDI is essential for improving and updating the drug
knowledge databases. Most investigation has focused on biological
relationships (genetic and protein interactions (PPI)) due mainly to
the availability of annotated corpora in the biological domain, a fact
that facilitates the evaluation of approaches. Few approaches have
focused on the extraction of DDIs.
In the last decade, Information Extraction techniques have received an
increasing interest as suitable solution to extract and analyse the
huge volume of published documents in the biological domain. The
BioCreAtIvE (Critical Assessment of Information Extraction systems in
Biology) challenges have played a key role in improving the
Information Extraction techniques applied to the biological domain by
providing a common benchmark for evaluating these techniques.
Recently, medical and pharmacological domain also benefit from the
application of such technology. However, there is no forum to allow
the comparison among the various techniques.

Likewise the BioCreative challenge evaluation has devoted to provide a
common frameworks for evaluation of text mining driving progress in
text mining techniques applied to the biological domain, our purpose
is to create a benchmark dataset and evaluation task that will enable
researchers to compare their algorithms applied to the extraction of
drug-drug interactions. The challenge task is intended to provide a
benchmarck forum for comparsing the latest advances of Information
Extraction techniques applied to the extraction of drug-drug
interactions. We will create a benchmark dataset and evaluation task
that will enable researchers to compare their algorithms.