ERNIE and Multi-Feature Fusion for News Topic Classification

Weisong Chen, Boting Liu, Weili Guan
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Abstract

Traditional news topic classification methods suffer from inaccurate text semantics, sparse text features and low classification accuracy. Based on this, this paper proposes a news topic classification method based on Enhanced Language Representation with Informative Entities (ERNIE) and multi-feature fusion. A semantically more accurate representation of text embedding is obtained by ERNIE. In addition, this paper extracts word, context and key sentence based on the news text. The key sentences of the news are obtained through the TextRank algorithm, which enables the model to focus on the content points of the news. Finally, this paper uses the attention mechanism to realize the fusion of multiple features. The proposed method is experimented on BBCNews. The experimental results show that we achieve classification accuracies superior to those of the compared methods, while validating the structural validity of the proposed method. The method in this paper has a positive effect on promoting the research of news topic classification.
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基于ERNIE和多特征融合的新闻主题分类
传统的新闻主题分类方法存在文本语义不准确、文本特征稀疏、分类准确率低等问题。在此基础上,提出了一种基于信息实体增强语言表示(ERNIE)和多特征融合的新闻主题分类方法。ERNIE在语义上获得了更准确的文本嵌入表示。此外,本文还根据新闻文本提取词、语境和关键句。通过TextRank算法获取新闻的关键句子,使模型能够专注于新闻的内容点。最后,利用注意机制实现多特征的融合。该方法在BBCNews上进行了实验。实验结果表明,该方法的分类精度优于其他方法,同时验证了该方法的结构有效性。本文的方法对新闻主题分类的研究具有积极的推动作用。
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