Fetching the paper…
Reading the bibliography…
Detecting events and classifying them into predefined types is an important step in knowledge extraction from natural language texts.
Dropout: a simple way to prevent neural networks from overfitting
Nitish Srivastava, Geoffrey E Hinton, Alex Krizhevsky, Ilya Sutskever, and Ruslan Salakhutdinov. 2014 · 1958
Earlier work this paper cites.
Bootstrap methods: another look at the jackknife
Bradley Efron. 1992 · 1992
Earlier work this paper cites.
A focused backpropagation algorithm for temporal
Michael C Mozer. 1995 · 1995
Earlier work this paper cites.
Long short-term memory
Sepp Hochreiter and Jürgen Schmidhuber. 1997 · 1997
Earlier work this paper cites.
The berkeley framenet project
Collin F Baker, Charles J Fillmore, and John B Lowe. 1998 · 1998
Earlier work this paper cites.
Joint event extraction via structured prediction with global features
Qi Li, Heng Ji, and Liang Huang. 2013 · 2013
Earlier work this paper cites.
Empirical evaluation of gated recurrent neural networks on sequence modeling
Junyoung Chung, Caglar Gulcehre, KyungHyun Cho, and Yoshua Bengio. 2014 · 2014
Earlier work this paper cites.
Adam: A method for stochastic optimization
Diederik Kingma and Jimmy Ba. 2014 · 2014
Cited alongside, same era.
Event extraction via dynamic multi-pooling convolutional neural networks
Yubo Chen, Liheng Xu, Kang Liu, Daojian Zeng, and Jun Zhao. 2015 · 2015
Cited alongside, same era.
Event detection and domain adaptation with convolutional neural networks
Thien Huu Nguyen and Ralph Grishman. 2015 · 2015
Cited alongside, same era.
Confidence interval for f1 measure of algorithm performance based on blocked 3×2 cross-validation
Yu Wang, Jihong Li, Ruibo Wang, and Wingli Yang. 2015 · 2015
Cited alongside, same era.
A language-independent neural network for event detection
Xiaocheng Feng, Lifu Huang, Duyu Tang, Heng Ji, Bing Qin, and Ting Liu. 2016 · 2016
Cited alongside, same era.
Joint event extraction via recurrent neural networks
Thien Huu Nguyen, Kyunghyun Cho, and Ralph Grishman. 2016 · 2016
Later among the works it cites.
Modeling skip-grams for event detection with convolutional neural networks
Thien Huu Nguyen and Ralph Grishman. 2016 · 2016
Later among the works it cites.
Exploiting argument information to improve event detection via supervised attention mechanisms
Shulin Liu, Yubo Chen, Kang Liu, and Jun Zhao. 2017 · 2017
Later among the works it cites.
Event detection via gated multilingual attention mechanism
Jian Liu, Yubo Chen, Kang Liu, and Jun Zhao. 2018 · 2018
Closest in time.
Graph convolutional networks with argument-aware pooling for event detection
Thien Huu Nguyen and Ralph Grishman. 2018 · 2018
Closest in time.
alphaXiv searches the wider corpus for related work and actual follow-ups.
alphaXiv is searching for related work…
Reza Ghaeini, Xiaoli Z Fern, Liang Huang, and Prasad Tadepalli. 2016 · 2016
Cited alongside, same era.
Leveraging framenet to improve automatic event detection
Shulin Liu, Yubo Chen, Shizhu He, Kang Liu, and Jun Zhao. 2016 · 2016
Cited alongside, same era.
Matthew E Peters, Mark Neumann, Mohit Iyyer, Matt Gardner, Christopher Clark, Kenton Lee, and Luke Zettlemoyer. 2018 · 2018
Closest in time.
Jointly extracting event triggers and arguments by dependency-bridge rnn and tensor-based argument interaction
Feng Qian, Lei Sha, Baobao Chang, and Zhifang Sui. 2018 · 2018
Closest in time.