基于问题与答案联合表示学习的半监督问题分类方法

问题分类旨在对问题的类型进行自动分类,该任务是问答系统研究的一项基本任务。该文提出了一种基于问题和答案联合表示学习的问题分类方法。该方法的特色在于利用问题及其答案作为共同的上下文环境,学习词的分布式表示,从而充分利用未标注样本中问题和答案隐含的分类信息。具体而言,首先,我们引入神经网络语言模型,利用问题与答案联合学习词向量表示,增加问题词向量的信息量;其次,加入大量未标注的问题与答案样本参与词向量学习,进一步增强问题词向量表示能力;最后,将已标注的问题样本以词向量形式表示作为训练样本,采用卷积神经网络建立问题分类模型。实验结果表明,该文提出的基于半监督问题分类方法能够充分利用词向量表示和大量未标注样本来提升性能,明显优于其他基准半监督分类方法。

Semi-supervised Question Classification with Jointly Learning #br# Question and Answer Representations

Question classification aims at classifying the types of questions automatically, which is essential to most question answering systems. This paper proposes a method of semi-supervised question classification with jointly learning question and answer representations. It is featured by considering the question and its corresponding answer as conjunct context to learn the word distributed representation. Specifically, neural network language model is introduced to learn question and answer representations jointly, so that the word vectors of question are added more information. Secondly, large numbers of unlabeled questions and answers participate in word vectors learning, which could strengthen the representation capacity of question word vectors. Finally, we represent the questions of word vectors as training samples, adopting the convolutional neural network to construct the question classifier. The experimental results demonstrate that the method of semi-supervised question classification with synergetic representations learning in this paper can make full use of word vectors and the unlabeled samples to improve the performance, and is better than other strong semi-supervised methods.