The CHEMDNER-patents task (BioCreative V - http://www.biocreative.org) is a
community challenge on named entity recognition of chemical compounds in
patents and text classification.
Lucene was used as a technology by teams that participated in previous
BioCreative challenges for indexing names (such as genes and proteins) in
articles or to store document vectors are stored as a Lucene index for text
classification purposes. We expect that Lucene will be a useful resource
also for the chemical compound recognition and indexing task.
We thus encourage Lucene users to participate at the chemical compound and
gene mention named entity recognition tasks of BioCreative V.
There is an increasing interest in the analysis of networks of named
entities automatically detected from unstructured data. In the biomedical
domain the main entity types that have been examined were genes, proteins,
chemicals and diseases, constructing entity co-occurrence networks derived
from mining the scientific literature. The CHEMDNER-patents task has the
main to promote the detection of chemical entities from patents.
Martin Krallinger, Spanish National Cancer Research Centre
Florian Leitner, Universidad Politecnica de Madrid
Obdulia Rabal, Center for Applied Medical Research (CIMA), University of
Julen Oyarzabal, Center for Applied Medical Research (CIMA), University of
Alfonso Valencia, Spanish National Cancer Research Centre
This task will address the automatic extraction of chemical and biological
data from medicinal chemistry patents. The identification and integration
of all information contained in these patents (e.g., chemical structures,
their synthesis and associated biological data) is currently a very hard
task not only for database curators but for life sciences researches and
biomedical text mining experts as well. Despite the valuable
characterizations of biomedical relevant entities such as chemical
compounds, genes and proteins contained in patents, academic research in
the area of text mining and information extraction using patent data has
been minimal. Pharmaceutical patents covering chemical compounds provide
information on their therapeutic applications and, in most cases, on their
primary biological targets.
This task would cover three essential steps for the identification of
biomedical relevant descriptions of chemical compounds:
· CEMP (chemical entity mention in patents, main task): the detection of
chemical named entity mentions in patents (start and end indices
corresponding to all the chemical entities).
· CPD (chemical passage detection, text classification task): the
detection of sentences that mention chemical compounds.
· GPRO (gene and protein related object task): for the GPRO task teams
have to identify mentions of gene and protein related objects (named as
GPROs) mentioned in patent tiles and abstracts.
Participating teams do not need to send results for all of three sub-tasks.
The can also send results only for individual sub-tasks.
CHEMDNER session at the BioCreative V workshop
At the BioCreative V Workshop to be held in Seville (Spain) September 9-11
(2015) there will be a session devoted to the CHEMDNER patents task. This
session will include an overview talk presenting the used datasets and
results obtained by the participating teams. A number of teams will also be
invited to present their systems. We plan to have also a discussion session
where teams, task organizers and domain experts will discuss the obtained
results and future steps. Finally during the poster session all teams will
be able to present their participating strategies.
CHEMDNER patents workshop proceedings and journal special issue
Participating teams will be invited to contribute to the: Proceedings of
the Fifth BioCreative Challenge Evaluation Workshop. A selected number of
top performing teams will also be invited to contribute with a system
description paper to a special issue of a relevant journal in the field.
Previous CHEMDNER (Biocreative IV)
The CHEMDNER-Biocreative IV special issue was published in the Journal of
Chemoinformatics: Volume 7 Supplement 1, 'Text mining for chemistry and the
CHEMDNER track'. It focused on the detection of chemical entities from
PubMed abstracts. The entire supplement is available from the Journal of
The special issue includes an overview paper on the task, a paper on the
CHEMDNER corpus and 13 selected systems description papers. Top scoring
teams obtained an F-score of 87.39% for the recognition of chemical entity
mentions, a very competitive result already close to the human IAA.
Additionally some systems could show additional improvements compared to
their original submissions.
Krallinger, M., Leitner, F., Rabal, O., Vazquez, M., Oyarzabal, J., &
Valencia, A. CHEMDNER: The drugs and chemical names extraction challenge.
Journal of Cheminformatics 2015, 7(Suppl 1):S1
Krallinger, M. et al. The CHEMDNER corpus of chemicals and drugs and its
annotation principles. Journal of Cheminformatics 2015, 7(Suppl 1):S2