面向文本结构的混合分层注意力网络的话题归类

针对目前话题归类模型中文本逻辑结构特征与文本组织结构特征利用不充分的问题,该文提出一种面向文本结构的混合分层注意力网络的话题归类模型(TSOHHAN)。文本结构包括逻辑结构和组织结构,文本的逻辑结构包括标题和正文等信息;文本的组织结构包括字—词语—句层次。TSOHHAN模型采用竞争机制融合标题和正文以增强文本逻辑结构特征在话题归类中的作用;同时该模型采用字-词语-句层次的注意力机制增强文本组织结构特征在话题归类中的作用。在4个标准数据集上的实验结果表明,TSOHHAN模型能够提高话题归类任务的准确率。

Text Structure Oriented Hybrid Hierarchical Attention Networks for Topic Classification

To better utilize text logical structure features and text organizational structure features in topic classification, this paper proposes a text structure oriented hybrid hierarchical attention network for this task. The logical structure usually includes information such as title and text, and the organizational structure includes character-word-sentence layer. The model integrates text headings and text bodies to improve the role of logical structure features in topic classification, and improves the role of text organizational structure features in topic classification based on the attention mechanism of char-sentence and word-sentence levels. Experimental results on 4 datasets show that the proposed model can improve the accuracy of topic classification tasks.