TY - JOUR
T1 - 需求驱动的云平台产品关键设计特征识别方法
AU - Su, Zhaojing
AU - Yu, Suihuai
AU - Chu, Jianjie
AU - Yu, Mingjiu
AU - Gong, Jing
AU - Huang, Yuexin
N1 - Publisher Copyright:
© 2021, Editorial Department of CIMS. All right reserved.
PY - 2021/12
Y1 - 2021/12
N2 - To improve the product key design knowledge discovery system of cloud service platform and further improve the matching efficiency of requirements and services, a product design key features recognition method based on Bidirectional Encoder Representations from Transformers (BERT) and random Lasso was proposed.First, the user feedback of real products was adopted in the experiment and was annotated manually.Based on BERT model, the output layer was built, and the named entity recognition model in the design field was trained to realize the automatic recognition of explicit design features.Experimental results showed that the proposed method could achieve better performance, precision and recall, and F1 scores were 90.55%, 97.16% and 93.68% respectively.Simultaneously, a new idea of knowledge transfer was proposed.In the current big data environment, the key design features contained in it could be mined and reused by using random lasso algorithm, so as to realize the accurate positioning of implicit design features.
AB - To improve the product key design knowledge discovery system of cloud service platform and further improve the matching efficiency of requirements and services, a product design key features recognition method based on Bidirectional Encoder Representations from Transformers (BERT) and random Lasso was proposed.First, the user feedback of real products was adopted in the experiment and was annotated manually.Based on BERT model, the output layer was built, and the named entity recognition model in the design field was trained to realize the automatic recognition of explicit design features.Experimental results showed that the proposed method could achieve better performance, precision and recall, and F1 scores were 90.55%, 97.16% and 93.68% respectively.Simultaneously, a new idea of knowledge transfer was proposed.In the current big data environment, the key design features contained in it could be mined and reused by using random lasso algorithm, so as to realize the accurate positioning of implicit design features.
KW - Bidirectional encoder representations from transformers
KW - Industrial design
KW - Named entity recognition
KW - Product design
KW - Random Lasso
KW - User requirements
UR - http://www.scopus.com/inward/record.url?scp=85122160133&partnerID=8YFLogxK
U2 - 10.13196/j.cims.2021.12.021
DO - 10.13196/j.cims.2021.12.021
M3 - 文章
AN - SCOPUS:85122160133
SN - 1006-5911
VL - 27
SP - 3604
EP - 3613
JO - Jisuanji Jicheng Zhizao Xitong/Computer Integrated Manufacturing Systems, CIMS
JF - Jisuanji Jicheng Zhizao Xitong/Computer Integrated Manufacturing Systems, CIMS
IS - 12
ER -