Topic Matching Algorithm Based on Multi-feature Fusion of Key Entities and Text Abstracts

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Topic Matching Algorithm Based on Multi-feature Fusion of Key Entities and Text Abstracts

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[1]JI Ke,ZHANG Xiu,MA Kun,et al.Topic Matching Algorithm Based on Multi-feature Fusion of Key Entities and Text Abstracts[J].Journal of Zhengzhou University (Engineering Science),2024,45(02):51-59.[doi:10.13705/j.issn.1671-6833.2024.02.008]

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Journal of Zhengzhou University (Engineering Science)[ISSN 1671-6833/CN 41-1339/T] Volume: 45 Number of periods: 2024 02 Page number: 51-59 Column: Public date: 2024-03-06

Title:: Topic Matching Algorithm Based on Multi-feature Fusion of Key Entities and Text Abstracts

Author(s):: JI Ke¹; 2; ZHANG Xiu¹; 2; MA Kun¹; 2; SUN Runyuan¹; 2; CHEN Zhenxiang¹; 2; WU Jun³; 1. School of Information Science and Engineering, University of Jinan, Jinan 250022, China; 2. Shandong Provincial Key Laboratory of Network Based Intelligent Computing, University of Jinan, Jinan 250022, China; 3. School of Computer and Information Technology, Beijing Jiaotong University, Beijing 100044, China

Keywords:: topic matching; key entity; text summary; text matching; information retrieval

CLC:: TP391. 1

DOI:: 10.13705/j.issn.1671-6833.2024.02.008

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 W2NER 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-sentence 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 performing 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 conducted 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.

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Last Update: 2024-03-08