Authors: Isabel Segura-Bedmar, Paloma Martínez, María Herrero-Zazo SemEval-2013: International Workshop on Semantic Evaluation (June 14-15, Atlanta, Georgia) SemEval-2013 Task 9: Extraction of Drug-Drug Interactions from Biomedical Texts (DDIExtraction 2013)

1. SemEval-2013: International Workshop on Semantic Evaluation
June 14-15, Atlanta, Georgia
SemEval-2013 Task 9: Extraction of Drug-Drug Interactions
from Biomedical Texts (DDIExtraction 2013)
Isabel Segura-Bedmar, Paloma Martínez, María Herrero-Zazo,
Computer Science Department, Universidad Carlos III de Madrid, Spain
{isegura, pmf, mhzazo}@inf.uc3m.es
Motivation:
A drug-drug interaction (DDI) occurs when one drug influences the level or activity of another drug. Drug Interaction databases are rarely
complete => Medical literature is the most effective source for the detection of drug interactions.
The goal of this shared task is to promote research on Information Extraction techniques applied to the pharmacovigilance domain.
ACKNOWLEDGMENTS:
This work was supported by the Regional Government of Madrid under the Research Network MA2VICMR [S2009/TIC-1542] and by the
Spanish Ministry of Education under the project MULTIMEDICA [TIN2010-20644-C03-01]. We would like to thank all participants for their
efforts and to congratulate them to their interesting work.
We thank the team at the Humboldt-Universitaet zu Berlin for making available a visualization of the DDI corpus using Stav:
http://http://corpora.informatik.hu-berlin.de/, https://github.com/TsujiiLaboratory/stav
Task 9.1. Recognition and Classification of
pharmacological substances
Task 9.1 Results
Task 9.2 Results
F1 F1
F1
F1
F1
F1
F1
F1
Test dataset: 54 DrugBank documents + 58 MedLine abstracts.
Gold standard annotations of pharmacological substances are provided to teams
both for training and test datasets.
Test dataset: 158 DrugBank documents + 33 MedLine abstracts.
Gold annotations entities were provided
Task 9.2. Extraction and Classification of drug-drug
interactions
Team Affiliation Approach
LASIGE University of Lisbon Conditional Random Fields
UEM_UC3M European University of Madrid, University Carlos
III of Madrid
Ontology-based approach
UMCC_DLSI Matanzas University, University of Alicante J48 classifier
Uturku University of Turku, Finland SVM classifier (TEES system)
WBI_NER Humboldt University of Berlin Conditional Random Fields Team Affiliation Approach
FBK-irst FBK-irst Hybrid kernel + scope of negations and semantic
roles
NIL_UCM University Complutense of Madrid SVM classifier
SCAI Fraunhofer SCAI SVM classifier
UC3M University Carlos III of Madrid Shallow Linguistic Kernel
UCOLORADO_SOM University of Colorado SMV classifier
Uturku University of Turku SVM classifier (TEES system)
UWM_TRIADS University of Wisconsin Two-stage SVM
WBI_DDI Humboldt University of Berlin Ensemble of kernels + TEES system + Moara system