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Combining context-relevant features with multi-stage attention network for short text classification

机译：结合上下文相关功能，具有多级注意网络，用于短文本分类

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摘要

Short text classification is a challenging task in natural language processing. Existing traditional methods using external knowledge to deal with the sparsity and ambiguity of short texts have achieved good results, but accuracy still needs to be improved because they ignore the context-relevant features. Deep learning methods based on RNN or CNN are hence becoming more and more popular in short text classification. However, RNN based methods cannot perform well in the parallelization which causes the lower efficiency, while CNN based methods ignore sequences and relationships between words, which causes the poorer effectiveness. Motivated by this, we propose a novel short text classification approach combining Context-Relevant Features with multi-stage Attention model based on Temporal Convolutional Network (TCN) and CNN, called CRFA. In our approach, we firstly use Probase as external knowledge to enrich the semantic representation for the solution to the data sparsity and ambiguity of short texts. Secondly, we design a multi-stage attention model based on TCN and CNN, where TCN is introduced to improve the parallelization of the proposed model for higher efficiency, and discriminative features are obtained at each stage through the fusion of attention and different-level CNN for a higher accuracy. Specifically, TCN is adopted to capture context-related features at word and concept levels, and meanwhile, in order to measure the importance of features, Word-level TCN (WTCN) based attention, Concept-level TCN (CTCN) based attention and different-level CNN are used at each stage to focus on the information of more important features. Finally, experimental studies demonstrate the effectiveness and efficiency of our approach in the short text classification compared to several well-known short text classification approaches based on CNN and RNN.
机译：短文本分类是自然语言处理中有挑战性的任务。使用外部知识来处理短文本的稀疏性和歧义的现有传统方法取得了良好的效果，但仍需要提高准确性，因为它们忽略了相关的功能。基于RNN或CNN的深度学习方法因此，在短文本分类中变得越来越受欢迎。然而，基于RNN的方法不能在并行化中表现良好，导致效率较低，而基于CNN的方法忽略了单词之间的序列和关系，这导致较差的效率。推荐，我们提出了一种基于时间卷积网络（TCN）和CNN的多阶段关注模型结合了新的简短文本分类方法，称为CRFA。在我们的方法中，我们首先使用probase作为外部知识来丰富解决方案的语义表示，以解决短信的数据稀疏性和歧义。其次，我们设计基于TCN和CNN的多阶段注意模型，其中引入TCN以改善所提出的型号的平行化，以获得更高效率的效率，并且通过融合和不同级别的CNN融合在每个阶段获得鉴别特征为了更高的准确性。具体而言，采用TCN在Word和概念级别中捕获与上下文相关的功能，同时，为了测量基于特征的重要性，基于Word级TCN（WTCN）的关注，概念级TCN（CTCN）的注意力和不同-Level CNN在每个阶段使用，专注于更重要的功能的信息。最后，实验研究表明，与基于CNN和RNN的几种众所周知的短文本分类方法相比，我们在短文本分类中的方法的有效性和效率。

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  • 来源
    《Computer speech and language》 |2022年第1期|101268.1-101268.14|共14页
  • 作者

    Yingying Liu; Peipei Li; Xuegang Hu;

  • 作者单位

    Key Laboratory of Knowledge Engineering with Big Data (Hefei University of Technology) Ministry of Education China School of Computer Science and Information Engineering Hefei University of Technology Hefei 230601 China;

    Key Laboratory of Knowledge Engineering with Big Data (Hefei University of Technology) Ministry of Education China School of Computer Science and Information Engineering Hefei University of Technology Hefei 230601 China;

    Key Laboratory of Knowledge Engineering with Big Data (Hefei University of Technology) Ministry of Education China School of Computer Science and Information Engineering Hefei University of Technology Hefei 230601 China Anhui Province Key Laboratory of Industry Safety and Emergency Technology Hefei Anhui 230601 China;

  • 收录信息 美国《科学引文索引》(SCI);美国《工程索引》(EI);
  • 原文格式 PDF
  • 正文语种 eng
  • 中图分类
  • 关键词

    Short text classification; Deep learning; Temporal Convolutional Network (TCN); Attention mechanism;

    机译：短文本分类;深度学习;时间卷积网络（TCN）;注意机制;

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