Share Email Print
cover

Proceedings Paper • new

Community conflict prediction method based on spliced BiLSTM
Format Member Price Non-Member Price
PDF $17.00 $21.00

Paper Abstract

Existing community conflict prediction models usually use a single unidirectional LSTM network to process graph and word embeddings simultaneously. However,there is no temporal coherence between graph and word embeddings. And their importance for prediction is different. A community conflict prediction method based on spliced bidirectional LSTM is proposed. Firstly, two bidirectional LSTMs are utilized to process graph and word embeddings respectively to break temporal dependency. Secondly, the hidden states of the two bidirectional LSTMs are weighted. Finally, the weighted hidden states are spliced and fed into subsequent layers of the neural network to predict conflicts. Experimental results show that this method can improve the AUC value to 0.733 on the Reddit dataset, and reduce the number of iterations of training.

Paper Details

Date Published: 27 November 2019
PDF: 6 pages
Proc. SPIE 11321, 2019 International Conference on Image and Video Processing, and Artificial Intelligence, 113211V (27 November 2019); doi: 10.1117/12.2540244
Show Author Affiliations
Si Chen, Guilin Univ. of Electronic Technology (China)
Xiaodong Cai, Guilin Univ. of Electronic Technology (China)
Bo Li, Guilin Univ. of Electronic Technology (China)
Zhenzhen Hou, Guilin Univ. of Electronic Technology (China)


Published in SPIE Proceedings Vol. 11321:
2019 International Conference on Image and Video Processing, and Artificial Intelligence
Ruidan Su, Editor(s)

© SPIE. Terms of Use
Back to Top