Open AccessAn approach to data tag optimization base on self-attention generative adversarial nets
Author(s) -
Yuan Chen,
Yafei Hou,
Bing Zhang
Publication year - 2020
Publication title -
journal of physics. conference series
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 0.21
H-Index - 85
eISSN - 1742-6596
pISSN - 1742-6588
DOI - 10.1088/1742-6596/1544/1/012195
Subject(s) - computer science , generative grammar , similarity (geometry) , consistency (knowledge bases) , adversarial system , generative adversarial network , function (biology) , artificial intelligence , data mining , machine learning , information retrieval , deep learning , image (mathematics) , evolutionary biology , biology
In order to solve the problem of low performance caused by errors and omissions in tags in the data resources, this paper proposes a tag optimization method based on self-attention generative adversarial nets(SAGAN). First use TF-IDF training to calculate the similarity between texts, then use the consistency between the similarity between texts and tag similarity to define the objective function. Second add correction terms to reduce the bias of user-provided tags before and after optimization. Finally, we propose to apply Self-Attention GAN to further improve the performance of tag optimization objective function, and the results are compared with the original tags. Compared with the original tag, the optimized tagging performance has been improved. The test results show that the tag is optimized and improved, and the performance problem in tag recommendation is solved.