Causal Relation Extraction of Uyghur Events Based on Bidirectional Long Short-term Memory Model

TIAN Shengwei; ZHOU Xingfa; YU Long; FENG Guanjun; Aishan WUMAIER; LI Pu

doi:10.11999/JEIT170402

Volume 40 Issue 1

Jan. 2018

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Article Navigation > Journal of Electronics & Information Technology > 2018 > 40(1): 200-208

TIAN Shengwei, ZHOU Xingfa, YU Long, FENG Guanjun, Aishan WUMAIER, LI Pu. Causal Relation Extraction of Uyghur Events Based on Bidirectional Long Short-term Memory Model[J]. Journal of Electronics & Information Technology, 2018, 40(1): 200-208. doi: 10.11999/JEIT170402

Citation:

TIAN Shengwei, ZHOU Xingfa, YU Long, FENG Guanjun, Aishan WUMAIER, LI Pu. Causal Relation Extraction of Uyghur Events Based on Bidirectional Long Short-term Memory Model[J]. Journal of Electronics & Information Technology, 2018, 40(1): 200-208. doi: 10.11999/JEIT170402

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Causal Relation Extraction of Uyghur Events Based on Bidirectional Long Short-term Memory Model

doi: 10.11999/JEIT170402

1.
(School of Software, Xinjiang University, Urumqi 830046, China)
2.
(Net Center, Xinjiang University, Urumqi 830046, China)

Funds:

The National Natural Science Foundation of China (61662074, 61563051, 61262064), The Key Project of National Natural Science Foundation of China (61331011), Xinjiang Uygur Autonomous Region Scientific and Technological Personnel Training Project (QN2016YX0051)

Received Date: 2017-05-02
Rev Recd Date: 2017-07-19
Publish Date: 2018-01-19

Abstract

Abstract

Since the traditional events causal relation has the disadvantages of small recognition coverage, a method for causal relation extraction of Uyghur events is presented based on Bidirectional Long Short-Term Memory (BiLSTM) model. In order to make full use of the event structure information, 10 characteristics of the Uyghur events structure information are extracted based on the study of the events causal relationship and Uyghur language features; At the same time, the word embedding is introduced as the input of BiLSTM to extract the deep semantic features of the Uyghur events and Batch Normalization (BN) algorithm is usded to accelerate the convergence of BiLSTM. Finally, concatenating these two kinds of features as the input of the softmax classifier to extract the Uyghur events causal relations. This method is used in the causal relation extraction of Uyghur events, and the results show that the precision rate, the recall rate and F value can reach 89.19 %, 83.19% and 86.09 %, indicating the effectiveness and practicability of the method of causal relation extraction of Uyghur events.
- Language signal processing,
- Event causal relation,
- Uyghur,
- Bidirectional Long Short-Term Memory (BiLSTM),
- Word embedding,
- Batch Normalization (BN)

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References(22)

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