The Case against Accuracy Estimation for Comparing Induction Algorithms
ICML '98 Proceedings of the Fifteenth International Conference on Machine Learning
Kernel methods for relation extraction
The Journal of Machine Learning Research
A practical part-of-speech tagger
ANLC '92 Proceedings of the third conference on Applied natural language processing
Kernel Methods for Pattern Analysis
Kernel Methods for Pattern Analysis
The relationship between Precision-Recall and ROC curves
ICML '06 Proceedings of the 23rd international conference on Machine learning
ROCR: visualizing classifier performance in R
Bioinformatics
Dependency tree kernels for relation extraction
ACL '04 Proceedings of the 42nd Annual Meeting on Association for Computational Linguistics
A shortest path dependency kernel for relation extraction
HLT '05 Proceedings of the conference on Human Language Technology and Empirical Methods in Natural Language Processing
RelEx---Relation extraction using dependency parse trees
Bioinformatics
Relation extraction and the influence of automatic named-entity recognition
ACM Transactions on Speech and Language Processing (TSLP)
Kernel-based learning for biomedical relation extraction
Journal of the American Society for Information Science and Technology
Methodological Review: Extracting interactions between proteins from the literature
Journal of Biomedical Informatics
Computational Biology and Chemistry
Techniques for evaluating fault prediction models
Empirical Software Engineering
A graph kernel for protein-protein interaction extraction
BioNLP '08 Proceedings of the Workshop on Current Trends in Biomedical Natural Language Processing
IEEE Transactions on Knowledge and Data Engineering
Comparative experiments on learning information extractors for proteins and their interactions
Artificial Intelligence in Medicine
Knowledge discovery from imbalanced and noisy data
Data & Knowledge Engineering
Journal of Biomedical Informatics
Learning relations from biomedical corpora using dependency trees
KDECB'06 Proceedings of the 1st international conference on Knowledge discovery and emergent complexity in bioinformatics
DTMBIO '10 Proceedings of the ACM fourth international workshop on Data and text mining in biomedical informatics
A comparison of machine learning techniques for detection of drug target articles
Journal of Biomedical Informatics
DDIExtractor: a web-based java tool for extracting drug-drug interactions from biomedical texts
NLDB'11 Proceedings of the 16th international conference on Natural language processing and information systems
Summary of Product Characteristics content extraction for a safe drugs usage
Journal of Biomedical Informatics
Combining tree structures, flat features and patterns for biomedical relation extraction
EACL '12 Proceedings of the 13th Conference of the European Chapter of the Association for Computational Linguistics
Combining dictionaries and ontologies for drug name recognition in biomedical texts
Proceedings of the 7th international workshop on Data and text mining in biomedical informatics
The DDI corpus: An annotated corpus with pharmacological substances and drug-drug interactions
Journal of Biomedical Informatics
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A drug-drug interaction (DDI) occurs when one drug influences the level or activity of another drug. Information Extraction (IE) techniques can provide health care professionals with an interesting way to reduce time spent reviewing the literature for potential drug-drug interactions. Nevertheless, no approach has been proposed to the problem of extracting DDIs in biomedical texts. In this article, we study whether a machine learning-based method is appropriate for DDI extraction in biomedical texts and whether the results provided are superior to those obtained from our previously proposed pattern-based approach [1]. The method proposed here for DDI extraction is based on a supervised machine learning technique, more specifically, the shallow linguistic kernel proposed in Giuliano et al. (2006) [2]. Since no benchmark corpus was available to evaluate our approach to DDI extraction, we created the first such corpus, DrugDDI, annotated with 3169 DDIs. We performed several experiments varying the configuration parameters of the shallow linguistic kernel. The model that maximizes the F-measure was evaluated on the test data of the DrugDDI corpus, achieving a precision of 51.03%, a recall of 72.82% and an F-measure of 60.01%. To the best of our knowledge, this work has proposed the first full solution for the automatic extraction of DDIs from biomedical texts. Our study confirms that the shallow linguistic kernel outperforms our previous pattern-based approach. Additionally, it is our hope that the DrugDDI corpus will allow researchers to explore new solutions to the DDI extraction problem.