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

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

1.
(新疆大学软件学院乌鲁木齐 830046) ②(新疆大学网络中心乌鲁木齐 830046) ③(新疆大学人文学院乌鲁木齐 830046) ④(新疆大学信息科学与工程学院乌鲁木齐 830046) ⑤(新疆大学语言学院乌鲁木齐 830046)

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)

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- 被引次数: 57
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

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

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)

摘要

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。
- 语言信号处理 /
- 事件因果关系 /
- 维吾尔语 /
- 双向LSTM /
- 词嵌入 /
- 批样规范化
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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