郑州大学学报（工学版）2024，Vol.45Issue(2)：51-59,9.DOI:10.13705/j.issn.1671-6833.2024.02.008
基于关键实体和文本摘要多特征融合的话题匹配算法
Topic Matching Algorithm Based on Multi-feature Fusion of Key Entities and Text Abstracts
摘要
Abstract
With the rapid popularization of the Internet,the amount of Internet news has increased dramatically.In this case,how to effectively find relevant reports that are more in line with a specific topic has become an urgent problem to be solved.To address this issue,a topic matching algorithm based on the fusion of key entities and text abstracts was proposed in this study.Firstly,the W2 NER model was used for named entity recognition to extract key entities using features such as word frequency,TF-IDF,lexical cohesion word-word similarity,and word-sen-tence similarity.Secondly,the Pegasus model was used for text summarization,and the deep semantic features of news texts were obtained by combining the key entity features with the text summary features using BiLSTM.Next,the cross-attention mechanism was employed to enhance the interaction between the matching news articles by per-forming feature interaction.Finally,the deep semantic features of the news texts and the text interaction features were fused together to participate in the determination of text topic matching.Comparative experiments were con-ducted on real data from Sohu,and the results showed that the proposed algorithm achieved similar accuracy and precision compared to other algorithms,while recall and F1 score were improved.关键词
话题匹配/关键实体/文本摘要/文本匹配/信息检索Key words
topic matching/key entity/text summary/text matching/information retrieval分类
信息技术与安全科学引用本文复制引用
纪科,张秀,马坤,孙润元,陈贞翔,邬俊..基于关键实体和文本摘要多特征融合的话题匹配算法[J].郑州大学学报（工学版）,2024,45(2):51-59,9.基金项目
国家自然科学基金资助项目(61702216,61772231) （61702216,61772231）
山东省重大科技创新工程项目(2021CXGC010103) （2021CXGC010103）
