Event extraction from cancer genetics literature

This paper attempts to employ learning based pattern classification technique to extract events from biological literature. Although various approaches to extract events have been explored, none is suitable for designing a practical system of event extraction. Extracting events more precisely is still an ongoing process. In this paper, new features that seem to be relevant for the given task are investigated. Two syntactic patterns namely phrase structure and dependency structure are explored to produce improved results with respect to the Cancer Genetics Data provided in the BioNLP´13 Shared Task. A stacked model based on conditional probability scores are also considered as features. The patterns and the probability scores along with some other linguistic features are fed to SVMs to train it for the task of bio-event extraction from natural language articles. The results are compared with the performance of the best extraction system in Cancer Genetics Task.