Making large-scale support vector machine learning practical
Advances in kernel methods
An introduction to variable and feature selection
The Journal of Machine Learning Research
MedPost: a part-of-speech tagger for bioMedical text
Bioinformatics
Robust, applied morphological generation
INLG '00 Proceedings of the first international conference on Natural language generation - Volume 14
Language independent NER using a maximum entropy tagger
CONLL '03 Proceedings of the seventh conference on Natural language learning at HLT-NAACL 2003 - Volume 4
Extracting protein-protein interactions using simple contextual features
BioNLP '06 Proceedings of the Workshop on Linking Natural Language Processing and Biology: Towards Deeper Biological Literature Analysis
Hierarchical hidden Markov models for information extraction
IJCAI'03 Proceedings of the 18th international joint conference on Artificial intelligence
Comparative experiments on learning information extractors for proteins and their interactions
Artificial Intelligence in Medicine
Learning to Learn Biological Relations from a Small Training Set
CICLing '09 Proceedings of the 10th International Conference on Computational Linguistics and Intelligent Text Processing
Edinburgh-LTG: TempEval-2 system description
SemEval '10 Proceedings of the 5th International Workshop on Semantic Evaluation
Agile corpus annotation in practice: an overview of manual and automatic annotation of CVs
LAW IV '10 Proceedings of the Fourth Linguistic Annotation Workshop
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An adaptable relation extraction system for the biomedical domain is presented. The system makes use of a large set of contextual and shallow syntactic features, which can be automatically optimised for each relation type. The system is tested on three different relation types; protein-protein interactions, tissue expression relations and fragment to parent protein relations.