基于知识图谱的图匹配文本分类

Graph Matching Text Classification Based on KG

【目的】在自然语言处理领域,文本分类是十分重要的基础研究,可以应用于许多下游任务中,例如文章检索、推荐系统、问答系统等。受到知识图谱在文本推理领域发挥作用的启发,本文探索了将知识图谱应用于文本分类任务的方法,在降低对标注训练数据依赖的同时利用知识图谱的推理能力提升文本分类的效果。【方法】本文提出了基于知识图谱的图匹配文本分类算法。具体而言,依据分类目标,为每一个类别构建了该类别的知识图谱,模型基于类别知识图谱中的语义和连接信息对文本与各个类别的相关性进行推理,综合各个知识图谱的推理评估结果。【结论】为了证明本文提出的方法的有效性,本文构建了分类所需的知识图谱并在两个数据集上进行了实验,实验结果证明在允许一定拒绝的前提下,此模型具有很高的准确率,进一步推动了算法的应用落地。

[Objective]In the field of natural language processing(NLP),text classification is a well-developed task that benefits many downstream tasks such as article retrieval,recommendation systems,and question answering.Inspired by the role of knowledge graph(KG)in the field of text reasoning,this article explores the way of utilizing the reasoning capability of KG to support text classification.[Methods]This paper proposes graph matching text classification based on KG.Specifically,this paper constructs the corresponding KG for each class according to the task.The model utilizes the semantics and structure information of these KGs to evaluate the relevance of the text to each class’s KG and then classifies the text by synthesizing the evaluations of all KGs.[Conclusions]In order to prove the effectiveness of our proposed model,this paper builds all KGs of classes in two datasets and conducts experiments on those datasets.The experiment results prove that the proposed model achieves high accuracy under the premise of allowing some data to be rejected and further promotes the application of the method.