Constructing Biological Knowledge Bases by Extracting Information from Text Sources
Proceedings of the Seventh International Conference on Intelligent Systems for Molecular Biology
Kernel methods for relation extraction
The Journal of Machine Learning Research
An integrated model of semantic and conceptual interpretation from dependency structures
COLING '00 Proceedings of the 18th conference on Computational linguistics - Volume 1
A comparison of parsing technologies for the biomedical domain
Natural Language Engineering
Journal of the American Society for Information Science and Technology - Bioinformatics
Extracting causal knowledge from a medical database using graphical patterns
ACL '00 Proceedings of the 38th Annual Meeting on Association for Computational Linguistics
Introduction to the CoNLL-2003 shared task: language-independent named entity recognition
CONLL '03 Proceedings of the seventh conference on Natural language learning at HLT-NAACL 2003 - Volume 4
Data Mining: Practical Machine Learning Tools and Techniques, Second Edition (Morgan Kaufmann Series in Data Management Systems)
MedTag: a collection of biomedical annotations
ISMB '05 Proceedings of the ACL-ISMB Workshop on Linking Biological Literature, Ontologies and Databases: Mining Biological Semantics
IntEx: a syntactic role driven protein-protein interaction extractor for bio-medical text
ISMB '05 Proceedings of the ACL-ISMB Workshop on Linking Biological Literature, Ontologies and Databases: Mining Biological Semantics
Representing sentence structure in hidden Markov models for information extraction
IJCAI'01 Proceedings of the 17th international joint conference on Artificial intelligence - Volume 2
Comparative experiments on learning information extractors for proteins and their interactions
Artificial Intelligence in Medicine
IJCNLP'05 Proceedings of the Second international joint conference on Natural Language Processing
Event extraction from trimmed dependency graphs
BioNLP '09 Proceedings of the Workshop on Current Trends in Biomedical Natural Language Processing: Shared Task
BioNLP '09 Proceedings of the Workshop on Current Trends in Biomedical Natural Language Processing
UVAVU: WordNet similarity and lexical patterns for semantic relation classification
SemEval '07 Proceedings of the 4th International Workshop on Semantic Evaluations
Evaluating the effects of treebank size in a practical application for parsing
SETQA-NLP '08 Software Engineering, Testing, and Quality Assurance for Natural Language Processing
The value of parsing as feature generation for gene mention recognition
Journal of Biomedical Informatics
Canadian AI'11 Proceedings of the 24th Canadian conference on Advances in artificial intelligence
Using a shallow linguistic kernel for drug-drug interaction extraction
Journal of Biomedical Informatics
Technological research plan for active ageing
Information Systems Frontiers
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In this paper we address the relation learning problem in the biomedical domain. We propose a representation which takes into account the syntactic information and allows for using different machine learning methods. To carry out the syntactic analysis, three parsers, LinkParser, Minipar and Charniak parser were used. The results we have obtained are comparable to the performance of relation learning systems in the biomedical domain and in some cases out-perform them. In addition, we have studied the impact of ensemble methods on learning relations using the representation we proposed. Given that recall is very important for the relation learning, we explored the ways of improving it. It has been shown that ensemble methods provide higher recall and precision than individual classifiers alone.