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| Matching Model for Technology Supply and Demand Texts Based on Multi-Layer Semantic Similarity |
Li Gang,Yu Hui,Mao Jin( ) |
| Center for Studies of Information Resources, Wuhan University, Wuhan 430072, China |
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Abstract [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.
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Received: 25 May 2021
Published: 20 January 2022
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| Fund:National Natural Science Foundation of China(71921002);National Key R&D Program of China(2018YFB1404300) |
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Corresponding Authors:
Mao Jin,ORCID：0000-0001-9572-6709
E-mail: maojin@whu.edu.cn
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