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基于双向LSTM的维吾尔语事件因果关系抽取

田生伟 周兴发 禹龙 冯冠军 艾山?吾买尔 李圃

田生伟, 周兴发, 禹龙, 冯冠军, 艾山?吾买尔, 李圃. 基于双向LSTM的维吾尔语事件因果关系抽取[J]. 电子与信息学报, 2018, 40(1): 200-208. doi: 10.11999/JEIT170402
引用本文: 田生伟, 周兴发, 禹龙, 冯冠军, 艾山?吾买尔, 李圃. 基于双向LSTM的维吾尔语事件因果关系抽取[J]. 电子与信息学报, 2018, 40(1): 200-208. doi: 10.11999/JEIT170402
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 and 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 and Information Technology, 2018, 40(1): 200-208. doi: 10.11999/JEIT170402

基于双向LSTM的维吾尔语事件因果关系抽取

doi: 10.11999/JEIT170402
基金项目: 

国家自然科学基金(61662074, 61563051, 61262064),国家自然科学基金重点项目(61331011),新疆自治区科技人才培养项目(QN2016YX0051)

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

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)

  • 摘要: 针对传统方法不能有效抽取维吾尔语事件因果关系的问题,该文提出一种基于双向LSTM(Bidirectional Long Short-Term Memory, BiLSTM)的维吾尔语事件因果关系抽取方法。通过对维吾尔语语言以及事件因果关系特点的研究,提取出10项基于事件内部结构信息的特征;同时为充分利用事件语义信息,引入词嵌入作为BiLSTM的输入,提取事件句隐含的深层语义特征并利用批样规范化(Batch Normalization, BN)算法加速BiLSTM的收敛;最后融合这两类特征作为softmax分类器的输入进而完成维吾尔语事件因果关系抽取。实验结果表明,该方法用于维吾尔语事件因果关系的抽取准确率为 89.19%, 召回率为 83.19%, F值为86.09%,证明了该文提出的方法在维吾尔语事件因果关系抽取上的有效性。
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    出版历程
    • 收稿日期:  2017-05-02
    • 修回日期:  2017-07-19
    • 刊出日期:  2018-01-19

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