Abstract
In the multi-label classification task (Automatic Tagging of Zhihu Questions), we present a classification system which includes five processes. Firstly, we use a preprocessing step to solve the problem that there is too much noise in the training dataset. Secondly, we choose several neural network models which proved effective in text classification task. Then we introduce k-max pooling structure to these models to fit this task. Thirdly, in order to obtain a better performance in ensemble process, we use an experiment-designing process to obtain classification results that are not similar to each other and all achieve relatively high scores. Fourthly, we use an ensemble process. Finally, we propose a method to estimate how many labels should be chosen. With these processes, our F1 score achieves 0.5194, which ranked No. 3.
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Acknowledgements
This work was supported in part by the National Project (2016YFB0801306) and the open source project (PyTorchText in GitHub). The authors would like to thank anonymous reviewers, Le Bo, Jiqiang Liu, Qiang Wang, YinQiao Li, YuXuan Rong and Chunliang Zhang for their comments.
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Xiang, Y., Wang, H., Ji, D., Zhang, Z., Zhu, J. (2018). NEUTag’s Classification System for Zhihu Questions Tagging Task. In: Zhang, M., Ng, V., Zhao, D., Li, S., Zan, H. (eds) Natural Language Processing and Chinese Computing. NLPCC 2018. Lecture Notes in Computer Science(), vol 11108. Springer, Cham. https://doi.org/10.1007/978-3-319-99495-6_24
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DOI: https://doi.org/10.1007/978-3-319-99495-6_24
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