Abstract
Multi-label topic classification aims to assign one or more relevant topic labels to a text. This paper presents the WiseTag system, which performs multi-label topic classification based on an ensemble of four single models, namely a KNN-based model, an Information Gain-based model, a Keyword Matching-based model and a Deep Learning-based model. These single models are carefully designed so that they are diverse enough to improve the performance of the ensemble model. In the NLPCC 2018 shared task 6 “Automatic Tagging of Zhihu Questions”, the proposed WiseTag system achieves an F1 score of 0.4863 on the test set, and ranks no. 4 among all the teams.
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Liang, G., Kao, H., Wing-Ki Leung, C., He, C. (2018). WiseTag: An Ensemble Method for Multi-label Topic Classification. 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 11109. Springer, Cham. https://doi.org/10.1007/978-3-319-99501-4_47
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DOI: https://doi.org/10.1007/978-3-319-99501-4_47
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