论文标题
基于知识的相似性矩阵CNN的简短文本分类通过知识供电的关注
Short Text Classification via Knowledge powered Attention with Similarity Matrix based CNN
论文作者
论文摘要
短文在网络上越来越受欢迎，例如聊天消息，SMS和产品评论。准确地对短文进行分类是一项重要且具有挑战性的任务。由于歧义性和数据稀疏性一词，许多研究在解决这个问题方面遇到了困难。为了解决这个问题，我们提出了一个基于相似性矩阵的卷积神经网络（KASM）模型的知识的关注，该模型可以通过使用知识和深层神经网络来计算全面信息。我们使用知识图（kg）来丰富短文本的语义表示，特别是在我们的模型中介绍了父母的信息。同时，我们考虑了短文本和标签表示之间的文字级别中的一词相互作用，并利用基于相似性矩阵的卷积神经网络（CNN）提取它。为了衡量知识的重要性，我们介绍了关注机制以选择重要信息。五个标准数据集的实验结果表明，我们的模型大大优于最先进的方法。
Short text is becoming more and more popular on the web, such as Chat Message, SMS and Product Reviews. Accurately classifying short text is an important and challenging task. A number of studies have difficulties in addressing this problem because of the word ambiguity and data sparsity. To address this issue, we propose a knowledge powered attention with similarity matrix based convolutional neural network (KASM) model, which can compute comprehensive information by utilizing the knowledge and deep neural network. We use knowledge graph (KG) to enrich the semantic representation of short text, specially, the information of parent-entity is introduced in our model. Meanwhile, we consider the word interaction in the literal-level between short text and the representation of label, and utilize similarity matrix based convolutional neural network (CNN) to extract it. For the purpose of measuring the importance of knowledge, we introduce the attention mechanisms to choose the important information. Experimental results on five standard datasets show that our model significantly outperforms state-of-the-art methods.
