Journal of Guangxi Normal University(Natural Science Edition) ›› 2021, Vol. 39 ›› Issue (2): 1-12.doi: 10.16088/j.issn.1001-6600.2020082603

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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

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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

CLC Number: 

  • TP391.3
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