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One for All: Neural Joint Modeling of Entities and Events

One for All: Neural Joint Modeling of Entities and Events

1 December 2018
T. Nguyen
Thien Huu Nguyen
ArXivPDFHTML

Papers citing "One for All: Neural Joint Modeling of Entities and Events"

6 / 6 papers shown
Title
Zero-Shot Transfer Learning for Event Extraction
Zero-Shot Transfer Learning for Event Extraction
Lifu Huang
Heng Ji
Kyunghyun Cho
Clare R. Voss
53
203
0
04 Jul 2017
Joint Extraction of Events and Entities within a Document Context
Joint Extraction of Events and Entities within a Document Context
Bishan Yang
Tom Michael Mitchell
50
247
0
12 Sep 2016
End-to-end Sequence Labeling via Bi-directional LSTM-CNNs-CRF
End-to-end Sequence Labeling via Bi-directional LSTM-CNNs-CRF
Xuezhe Ma
Eduard H. Hovy
90
2,651
0
04 Mar 2016
Learning Phrase Representations using RNN Encoder-Decoder for
  Statistical Machine Translation
Learning Phrase Representations using RNN Encoder-Decoder for Statistical Machine Translation
Kyunghyun Cho
B. V. Merrienboer
Çağlar Gülçehre
Dzmitry Bahdanau
Fethi Bougares
Holger Schwenk
Yoshua Bengio
AIMat
1.0K
23,338
0
03 Jun 2014
Efficient Estimation of Word Representations in Vector Space
Efficient Estimation of Word Representations in Vector Space
Tomas Mikolov
Kai Chen
G. Corrado
J. Dean
3DV
650
31,490
0
16 Jan 2013
ADADELTA: An Adaptive Learning Rate Method
ADADELTA: An Adaptive Learning Rate Method
Matthew D. Zeiler
ODL
136
6,626
0
22 Dec 2012
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