融合量子干涉信息的双重特征文本表示模型  

Quantum Interference Based Duet-Feature Text Representation Model

作　　者：高珲 张鹏[1] 张静[1] GAO Hui;ZHANG Peng;ZHANG Jing(College of Intelligence and Computing,Tianjin University,Tianjin 300350)

机构地区：[1]天津大学智能与计算学部,天津300350

出　　处：《模式识别与人工智能》2024年第2期172-180,共9页Pattern Recognition and Artificial Intelligence

基　　金：国家自然科学基金项目(No.62276188,61876129);天津大学-中科闻歌联合实验室项目资助。

摘　　要：在信息检索领域,量子干涉理论已应用于文档相关性、次序效应等核心问题的研究中,旨在建模用户认知引起的类量子干涉现象.文中从语言理解的需求出发,利用量子理论的数学工具分析语义组合过程中存在的语义演化现象,提出融合量子干涉信息的双重特征文本表示模型(Quantum Interference Based Duet-Feature Text Representation Model,QDTM).模型以约化密度矩阵为语言表示的核心组件,有效建模维度级别的语义干涉信息.在此基础上,构建捕获全局特征信息与局部特征信息的模型结构,满足语言理解过程中不同粒度的语义特征需求.在文本分类数据集和问答数据集上的实验表明,QDTM的性能优于量子启发的语言模型和神经网络文本匹配模型.In the field of information retrieval,quantum interference theory is applied to the study of core issues such as document relevance and order effects,aiming at modeling quantum-like interference phenomena caused by user cognition.Based on the language understanding task,the mathematical tools of quantum theory are utilized to analyze the semantic evolution phenomenon in the semantic combination process.A quantum interference based duet-feature text representation model(QDTM)is proposed.The reduced density matrix is taken as the core component of language representation to effectively model semantic interference information at the dimension-level.On this basis,a model structure is constructed to capture global and local feature information,meeting the semantic feature requirements of different granularities in the language understanding process.Experiments on text classification datasets and question and answering datasets show that QDTM outperforms quantum-inspired language models and neural network text matching models.

关 键 词：量子干涉 文本匹配 约化密度矩阵 语义表示 自然语言处理 

分 类 号：TP391[自动化与计算机技术—计算机应用技术]