Biological event recognition with textual induction

This paper describes a supervised approach to the recognition of biological events, which combines statistical sequential labelling and symbolic event extractionrules. Bottom-up textual induction has been applied to generating eventextraction rules. As an evaluation data set, we use a corpus of biomedical abstracts, in which biological events concerning gene regulation in E. coli and H. Sapiens have been annotated by a group of biologists. The event instance extraction performance has been evaluated using 10- fold cross validation. The experimental results show that named entity recognition (NER) and semantic role labelling (SRL) performance are close to annotator performance, as indicated by the interannotator agreement (IAA) scores, whereas automatic event extraction performance is around 28%, as compared to 40% IAA for exact manual event extraction.