Abstract
Classification and treatment of vehicle defect complaint data is an important link in the process of vehicle recall. Traditionally, the complaint data is classified by keyword matching method based on defect label library during the process of dealing with vehicle complaint data, which heavily relies heavily on the quality of the vehicle defect label library. The speed of traditional classification methods is rapid, but the accuracy is low. We transform the classification task of vehicle complaint data into a multi-label classification problem. Multi-label classification of vehicle defect information collection based on seq2seq model named VDIF-M is proposed in this paper. Firstly, a synonymous vehicle defect description label library is constructed based on the vehicle defect description data and vehicle domain corpus collected from various channels. Then a seq2seq model is proposed to solve the problem of multi-label classification of vehicle complaint data, which fuses the distribution relationship between labels. Substantial experimental results show that the proposed method outperforms previous methods in multi-label classification of vehicle complaint data.
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Acknowledgments
This work is supported by National Natural Science Foundation of China under Grants No. 61671070, National Science Key Lab Fund project 6142006190301, National Language Committee of China under Grants ZDI135-53, and Project of Three Dimension Energy Consumption Saving Strategies in Cloud Storage System in Promoting the Developing University Intension–Disciplinary Cluster No. 5211910940.
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© 2019 ICST Institute for Computer Sciences, Social Informatics and Telecommunications Engineering
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You, X., Zhang, Y., Li, B., Lv, X., Han, J. (2019). VDIF-M: Multi-label Classification of Vehicle Defect Information Collection Based on Seq2seq Model. In: Yin, Y., Li, Y., Gao, H., Zhang, J. (eds) Mobile Computing, Applications, and Services. MobiCASE 2019. Lecture Notes of the Institute for Computer Sciences, Social Informatics and Telecommunications Engineering, vol 290. Springer, Cham. https://doi.org/10.1007/978-3-030-28468-8_8
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DOI: https://doi.org/10.1007/978-3-030-28468-8_8
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