TY  - CONF
ID  - SisLab1550
UR  - https://eprints.uet.vnu.edu.vn/eprints/id/eprint/1550/
A1  - Tran, Mai Vu
A1  - Le, Hoang Quynh
A1  - Phi, Van Thuy
A1  - Pham, Thanh Binh
A1  - Nigel, Collier
Y1  - 2016/03/26/
N2  - 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.
TI  - Exploring a Probabilistic Earley Parser for Event Composition in Biomedical Texts
M2  - Hanoi
AV  - public
T2  - SW4PHD: the 2016 Scientific Workshop for PhD Students
ER  -