基于互信息最大化的文档相关性模型  

Document relevance model based on mutual information maximization

作　　者：陆敏锋 张启飞[2] 卢成浪 吴菁 LU Min-feng;ZHANG Qi-fei;LU Cheng-lang;WU Jing(School of Modern Information Technology,Zhejiang Institute of Mechanical and Electrical Engineering,Hangzhou 310053,China;School of Software Technology,Zhejiang University,Ningbo 315048,China)

机构地区：[1]浙江机电职业技术学院现代信息技术学院,浙江杭州310053 [2]浙江大学软件学院,浙江宁波315048

出　　处：《计算机工程与设计》2024年第9期2712-2718,共7页Computer Engineering and Design

基　　金：教育部人文社科研究青年基金项目(21YJCZH096);杭州市哲学社会科学规划课题基金项目(M22JC067)。

摘　　要：针对传统的文档相关性识别方法在训练过程中文档信息提取不充分的问题,提出一种互信息最大化框架下的深度学习方法。涉及一个无监督学习过程,使用全局和局部互信息学习文档表示,最大化神经网络的输入表示和输出表示之间的互信息,提取更为丰富的文档内容和结构信息,优化模型预测结果。经过在多项任务上的评估,实验结果表明了该方法的可行性和有效性,与一些传统的方法相比,其在准确度上有相当或更好的表现。To address the issue of inadequate information extraction from documents during the training process of traditional document relevance identification methods,a deep learning method based on maximizing mutual information was proposed.An unsupervised learning process that utilized global and local mutual information to learn document representations was involved,with the aim of maximizing the mutual information between the input and output representations of the neural network.The extraction of more comprehensive document content and structural information was enabled,resulting in improved model prediction results.Evaluation on multiple tasks demonstrates the feasibility and effectiveness of the proposed method which performs comparably or even better in accuracy than that of some traditional methods.

关 键 词：互信息 文档表示 文档相关性 最大化 无监督学习 残差网络 全局特征 

分 类 号：TP391[自动化与计算机技术—计算机应用技术]