Event Detection with Neural Networks: A Rigorous Empirical Evaluation

Walker Orr, Prasad Tadepalli, Xiaoli Fern

Abstract
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.
Anthology ID:
D18-1122
Volume:
Proceedings of the 2018 Conference on Empirical Methods in Natural Language Processing
Month:
October-November
Year:
2018
Address:
Brussels, Belgium
Editors:
Ellen Riloff, David Chiang, Julia Hockenmaier, Jun’ichi Tsujii
Venue:
EMNLP
SIG:
SIGDAT
Publisher:
Association for Computational Linguistics
Note:
Pages:
999–1004
Language:
URL:
https://aclanthology.org/D18-1122
DOI:
10.18653/v1/D18-1122
Bibkey:
Cite (ACL):
Walker Orr, Prasad Tadepalli, and Xiaoli Fern. 2018. Event Detection with Neural Networks: A Rigorous Empirical Evaluation. In Proceedings of the 2018 Conference on Empirical Methods in Natural Language Processing, pages 999–1004, Brussels, Belgium. Association for Computational Linguistics.
Cite (Informal):
Event Detection with Neural Networks: A Rigorous Empirical Evaluation (Orr et al., EMNLP 2018)
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https://preview.aclanthology.org/landing_page/D18-1122.pdf
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