结合对维吾尔语语言的特点分析,该文提出一种基于深度卷积神经网络(deep convolutional neural networks,DCNNs)联合长短期记忆网络(long-short term memory,LSTM)实现的维吾尔语文本突发事件识别方法。该方法提取突发事件包含六大特征块,并在特征集中引入富含词汇语义及上下文位置关系的Word Embedding,利用DCNNs对黏着性语言特征抽象化的学习能力抽取事件句中的高阶局部特征,以此作为LSTM网络的输入,利用其对于事件句中抽象含义序列关系的捕获特性获取全局特征,训练 Softmax分类器完成维吾尔语突发事件的识别任务。该方法在维吾尔语突发事件识别中的准确率达到80.60%,召回率81.39%,F值80.99%。实验结果表明,与不同层数的DCNNs和独立的LSTM网络相比,DCNNs-LSTM模型更具备挖掘隐含上下文深层语义信息的能力,对Word Embedding特征项的引入有效地提高了模型识别性能。
Abstract
A deep convolutional neural networks (DCNNs) combined with long-short term memory (LSTM) is proposed to extract the emergency events in Uyghur text. The method extracts six major feature blocks that are included in emergency events and employs word embedding. Using the DCNNs to extract the high level local features of the event sentence as the input,this method captures the sequence relations in the event sentence via LSTM,and train a Softmax classifier to accomplish the task. The accuracy of the method is 80.60%,the recall 81.39%,and the F value 80.99%.
关键词
维吾尔语 /
突发事件识别 /
深度卷积神经网络 /
长短期记忆网络 /
word embedding
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Key words
Uyghur /
emergency recognition /
deep convolutional neural network /
long-short memory term /
word embedding
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脚注
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基金
国家自然科学基金(61662074,61563051,61262064,61331011);新疆维吾尔自治区科技人才培养项目(QN2016YX005)
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