@inproceedings{gao-2020-deep,
    title = "Deep Hierarchical Classification for Category Prediction in {E}-commerce System",
    author = "Gao, Dehong",
    editor = "Malmasi, Shervin  and
      Kallumadi, Surya  and
      Ueffing, Nicola  and
      Rokhlenko, Oleg  and
      Agichtein, Eugene  and
      Guy, Ido",
    booktitle = "Proceedings of the 3rd Workshop on e-Commerce and NLP",
    month = jul,
    year = "2020",
    address = "Seattle, WA, USA",
    publisher = "Association for Computational Linguistics",
    url = "https://preview.aclanthology.org/jlcl-multiple-ingestion/2020.ecnlp-1.10/",
    doi = "10.18653/v1/2020.ecnlp-1.10",
    pages = "64--68",
    abstract = "In e-commerce system, category prediction is to automatically predict categories of given texts. Different from traditional classification where there are no relations between classes, category prediction is reckoned as a standard hierarchical classification problem since categories are usually organized as a hierarchical tree. In this paper, we address hierarchical category prediction. We propose a Deep Hierarchical Classification framework, which incorporates the multi-scale hierarchical information in neural networks and introduces a representation sharing strategy according to the category tree. We also define a novel combined loss function to punish hierarchical prediction losses. The evaluation shows that the proposed approach outperforms existing approaches in accuracy."
}