Event Detection with Neural Networks: A Rigorous Empirical Evaluation

08/26/2018
by   J. Walker Orr, et al.
0

Detecting events and classifying them into predefined types is an important step in knowledge extraction from natural language texts. While the neural network models have generally led the state-of-the-art, the differences in performance between different architectures have not been rigorously studied. In this paper we present a novel GRU-based model that combines syntactic information along with temporal structure through an attention mechanism. We show that it is competitive with other neural network architectures through empirical evaluations under different random initializations and training-validation-test splits of ACE2005 dataset.

READ FULL TEXT

Please sign up or login with your details

Forgot password? Click here to reset