Matching Model for Technology Supply and Demand Texts Based on Multi-Layer Semantic Similarity

被引：0
|
作者：
Li G. [1 ]
Yu H. [1 ]
Mao J. [1 ]
机构：
[1] Center for Studies of Information Resources, Wuhan University, Wuhan
来源：
Data Analysis and Knowledge Discovery | 2021年 / 5卷 / 12期
基金：
中国国家自然科学基金;
关键词：
Data Fusion; Supply and Demand Matching; Technological Text for Supply and Demand; Technology Transfer;
D O I：
10.11925/infotech.2096-3467.2021.0524
中图分类号：
学科分类号：
摘要：
[Objective] This paper proposes a new high-accuracy-model, aiming to improve the matching of technology supply and demand texts and promote technology transfer. [Methods] First, we separated the titles and texts as two structure levels. Then, we calculated the word similarity and sentence similarity through a variety of methods. Finally, we constructed a Multi-layer Semantic Text Matching (MSTM) model based on multi-layer semantic similarity and the deep learning model. [Results] We found that different level of information yielded different matching results. The accuracy of MSTM was 96.50%, which was higher than single BERT (90.70%), DSSM (87.80%), and ESIM (87.50%). [Limitations] Our new model only considers two levels of text structures. [Conclusions] This new model can help online technology trading services match supply and demand, as well as promote technology transfer. © 2021, Chinese Academy of Sciences. All rights reserved.
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收藏
页码：25 / 36
页数：11
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