基于变分自编码器的问题识别方法OA北大核心CSTPCD
Question Detection Method Based on Variational Auto-encoder
在非正式问答语料中,往往存在问题文本中包含多个子问题的情况,需要将每个子问题分别识别出来.由于标注样本的数目太小,并且存在海量的未标注样本,可以用半监督深度学习方法来进行问题识别.采用了变分自编码器(variational auto-encoder, VAE),并且结合了在深度学习模型中广泛应用的注意力机制.实验结果表明,不管是F值还是准确率,变分自编码器和注意力机制的结合可以显著地提升问题识别的性能.
In informal question-answer corpus, there were many questions which contained several sub-questions. These questions should be indentified. Due to the number of the labeled samples was so small and there was a bulk of unlabeled samples, a semi-supervised deep learning method was used to detect questions. Variational auto-encoder ( VAE) and attention mechanism were employed, and the latter one was widely used in deep learning methods. The effectiv…查看全部>>
王路;李寿山
苏州大学 计算机科学与技术学院 江苏 苏州215006苏州大学 计算机科学与技术学院 江苏 苏州215006
信息技术与安全科学
非正式问题识别半监督变分自编码器注意力机制
informalquestion detectionsemi-supervisedVAEattention mechanism
《郑州大学学报（理学版）》 2019 (3)
文本情绪分类的资源建设及关键技术研究
79-84,6
国家自然科学基金项目(61331011,61672366).
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