Event detection with forward-backward recurrent neural networks

Graduate Thesis Or Dissertation

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Citeable URL: https://ir.library.oregonstate.edu/concern/graduate_thesis_or_dissertations/j9602530w

Descriptions

Attribute Name	Values
Creator	Ghaeini, Mohammad Reza
Abstract	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 described with a phrase. In this thesis, we introduce and apply 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. Keywords: Deep Learning, Event Detection
License	All rights reserved
Resource Type	Masters Thesis
Date Available	2017-06-26T22:54:44+00:00
Date Issued	2017-05-12
Degree Level	Master's
Degree Name	Master of Science (M.S.)
Degree Field	Computer Science
Degree Grantor	Oregon State University
Commencement Year	2017
Advisor	Fern, Xiaoli Z.
Committee Member	Huang, Liang Coop, Len Hendrix, David A. Tadepalli, Prasad
Academic Affiliation	Electrical Engineering and Computer Science
Non-Academic Affiliation	Oregon State University. Graduate School
Dichiarazione dei diritti	In Copyright
Publisher	Oregon State University
Peer Reviewed	No
Language	English [eng]
Replaces	http://hdl.handle.net/1957/61576

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