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MS Final Examination – Mohammad Reza Ghaeini


Friday, May 12, 2017 10:00 AM - 12:00 PM

Event Detection using Forward-Backward Recurrent Neural Networks
Automatic event extraction from natural text is an important and challenging task for natural language understanding. Traditional event detection methods heavily rely on manually engineered rich features. Recent deep learning approaches alleviate this problem by automatic feature engineering. But such efforts, like tradition methods, have so far only focused on single-token event mentions, whereas in practice events can also be a phrase. In this thesis, we instead introduce and use forward-backward recurrent neural networks (FBRNNs) to detect events that can be either words or phrases. Experimental results demonstrate that FBRNN is competitive with the state-of-the-art methods on the ACE 2005 and the Rich ERE 2015 event detection tasks.

Major Advisor: Xiaoli Fern
Committee: Liang Huang
Committee: Prasad Tadepalli
Committee: David Hendrix
GCR: Leonard Coop


Kelley Engineering Center (campus map)
1007
Calvin Hughes
1 541 737 3168
Calvin.Hughes at oregonstate.edu
Sch Elect Engr/Comp Sci
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