广西师范大学学报（哲学社会科学版） ›› 2021, Vol. 39 ›› Issue (2): 1-12.doi: 10.16088/j.issn.1001-6600.2020082603

• CCIR2020 •    下一篇

神经信息检索模型建模因素综述

杨州1,2, 范意兴3, 朱小飞1*, 郭嘉丰3, 王越2   

  1. 1.重庆理工大学 计算机科学与工程学院, 重庆 400054;
    2.搜狐公司智能媒体研发中心, 北京 100190;
    3.中国科学院计算技术研究所 网络数据科学与技术重点实验室, 北京 100190
  • 收稿日期:2020-08-26 修回日期:2020-09-22 出版日期:2021-03-25 发布日期:2021-04-15
  • 通讯作者: 朱小飞(1979—),男,江苏扬州人,重庆理工大学教授,博士。E-mail:zxf@cqut.edu.cn
  • 基金资助:
    国家自然科学基金(61722211,61502065);重庆市基础科学与前沿技术研究项目(cstc2017jcyjBX0059,cstc2017jcyjAX0339);重庆市教委语言文字科研项目重点项目(yyk20103)

Survey on Modeling Factors of Neural Information Retrieval Model

YANG Zhou1,2, FAN Yixing3, ZHU Xiaofei1*, GUO Jiafeng3, WANG Yue2   

  1. 1. School of Computer Science and Engineering, Chongqing University of Technology, Chongqing 400054, China;
    2. Intelligent Media R & D Center SOHU, Beijing 100190, China;
    3. CAS Key Lab of Network Data Science and Technology, Institute of Computing Technology, Chinese Academy of Sciences, Beijing 100190, China
  • Received:2020-08-26 Revised:2020-09-22 Online:2021-03-25 Published:2021-04-15

摘要： 信息检索模型被广泛运用于搜索引擎中,且在工业领域被广泛应用。信息检索任务中,模型对信号量的侧重建模导致模型指标差异巨大。目前模型大部分基于以下部分或全部信息建模:精确信号量、相似信号量、信号量区分度、查询词权重、临近量、文本结构信息、不同分布假设。本文介绍各个建模因素的具体含义,并通过引用相关实验例证该因素对于建模起到的积极作用。基于以上实验及分析,最后对信息检索模型的未来发展及趋势作进一步讨论和分析。

关键词: 信息检索, 深度学习, 卷积神经网络, 循环神经网络, 综述

Abstract: Information retrieval models are widely used in search engines. In the task of information retrieval, these models focuses on the different semaphores, which leads to great differences in model performance. At present, most models are based on part or all of the following information: exact signals, similar signals, signals differentiation, query word weight, proximity, text structure, and different distribution assumptions. This paper introduces the specific meaning of each modeling factor, and exemplifies the positive effect of this factor on modeling through relevant experiments. Based on the above experiments and analysis, this paper finally discusses and analyzes the future development and the trend of information retrieval model.

Key words: information retrieval, deep learning, convolutional neural network, recurrent neural network, survey

中图分类号: 

  • TP391.3
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