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- W4225751926 abstract "A new talent training mode of collaboration between industry and education was proposed, which aims to reduce the gaps of talent definition between enterprise demand and college education. We formulates scenario as a text matching task of similarity between courses details and job position information, developed a ERNIE+DPCNN based model for the task. The model has been benchmarked on LCQMC dataset, the results show that it achieves the art-of state performance of Acc 0.8809, Recall 0.8745, and F1 score 0.8739, We conducted many experiments about the text similarity calculation between domains of industry and educations. The experiment results show our approach has much potential to optimize the curriculum system while applying in this field." @default.
- W4225751926 created "2022-05-05" @default.
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- W4225751926 date "2022-01-01" @default.
- W4225751926 modified "2023-10-17" @default.
- W4225751926 title "Text Matching application for integration between industry and education" @default.
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- W4225751926 doi "https://doi.org/10.1109/icmtma54903.2022.00145" @default.
- W4225751926 hasPublicationYear "2022" @default.
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