%A Mai Vu Tran
%A Hoang Quynh Le
%A Van Thuy Phi
%A Thanh Binh Pham
%A Collier Nigel
%T Exploring a Probabilistic Earley Parser for Event Composition in Biomedical Texts
%X We describe a high precision system for extracting events of biomedical significance that was developed during the BioNLP shared task 2013 and tested on the Cancer Genetics data set. Our system explored a multi-stage approach including trigger detection, edge detection and event composition. After trigger edge detection is finished we are left with a semantic graph from which we must select the optimal subset that is consistent with the semantic frames for each event type.  The system achieved an F-score on the development data of 73.67 but was ranked 5th out of six with an F-score of 29.94 on the test data. How-ever, precision was the second highest ranked on the task at 62.73. Analysis suggests the need to continue to improve our system for complex events particularly taking into account cross-domain differences in argument distributions.
%C Hanoi
%D 2016
%L SisLab1550