文献类型：期刊文献

中文题名：融合知识图谱和多模态的文本分类研究

英文题名：Research on Text Classification Based on Knowledge Graph and Multimodal

作者：Jing, Li[1]; Yao, Ke[1]

第一作者：景丽

机构：[1] School of Computer and Information Engineering, Henan University of Economics and Law, Zhengzhou, 450046, China

第一机构：河南财经政法大学计算机与信息工程学院

年份：2024

卷号：59

期号：2

起止页码：102-109

外文期刊名：Computer Engineering and Applications

收录：EI(收录号：20241215780807)

语种：中文

外文关键词：Classification (of information) - Image enhancement - Knowledge graph - Learning systems - Modal analysis - Semantics - Signal encoding

摘要：Traditional text classification methods are mainly empirical statistical learning methods driven by single-modal data, which lack the ability to understand the data, and have poor robustness. The single-modal input is also difficult to effectively analyze the increasingly rich multi-modal data in the Internet. To solve this problem, two methods to improve the classification ability are proposed：introducing multi-modal information into the model input in order to make up for the limitation of single-modal information; introducing knowledge graph entity information into the model input aims to enrich the semantic information of the text and improve model’s generalization ability. The model uses BERT to extract text features, improved ResNet to extract image features, and TransE to extract text entity features, which are input into the BERT model for classification through early fusion. On the MM-IMDB data set which studies the multi label classification problem, the F1 score reaches 66.5%, on the Twitter15&17 data set which studies sentiment analysis problem, the ACC score reaches 71.1%, and the results are better than other models. Experimental results show that introducing multimodal information and entity information can improve the text classification ability of the model. ? 2024 Journal of Computer Engineering and Applications Beijing Co., Ltd.; Science Press. All rights reserved.