融入新闻标题信息的新闻文本与评论的语义相似度计算方法  

Semantic Similarity Calculation Method of News Text and Comment Integrated with News Title Information

作　　者：李伊仝 王红斌[1] 程良 LI Yitong;WANG Hongbin;CHENG Liang(Faculty of Information Engineering and Automation,Kunming University of Science and Technology,Kunming 650504,China;College of City,Kunming University of Science and Technology,Kunming 650051,China)

机构地区：[1]昆明理工大学信息工程与自动化学院,昆明650504 [2]昆明理工大学城市学院,昆明650051

出　　处：《吉林大学学报（理学版）》2022年第6期1399-1406,共8页Journal of Jilin University:Science Edition

基　　金：国家自然科学基金(批准号:61966020);云南省基础研究计划面上项目(批准号:CB22052C143A);云南省教育厅科学研究基金(批准号:2018JS035).

摘　　要：针对预训练模型在处理新闻这种长文本时会截断一部分文本,导致文本信息缺失的问题,提出一种在融入新闻标题信息基础上将TextRank算法、隐含Dirichlet分布主题模型与预训练模型相结合的方法构建模型,并将该模型与其他语义相似度计算方法进行对比.结果表明,该模型准确率为82.46%,召回率为87.43%,精确率为82.68%,F 1值为84.99%,取得了最优结果,从而有效提高了新闻文本与评论的语义相似度计算性能.Aiming at the problem that the pre-training model would cut off part of text when dealing with long text such as news,which led to the loss of text infomation,we proposed a method to build a model by combining TextRank algorithm,implicit Dirichlet distribution topic model and pre-training model on the basis of integrating news title information,and compared the model with other semantic similarity calculation methods.The results show that the accuracy rate of the model is 82.46%,the recall rate is 87.43%,the accuracy rate is 82.68%,and the F 1 value is 84.99%,the optimal results are obtained,which effectively improves the performance of semantic similarity calculation between news texts and comments.

关 键 词：语义相似度 预训练模型 隐含Dirichlet分布 新闻评论 

分 类 号：TP391.1[自动化与计算机技术—计算机应用技术]