@inproceedings{karn-etal-2017-end,
    title = "End-to-End Trainable Attentive Decoder for Hierarchical Entity Classification",
    author = {Karn, Sanjeev  and
      Waltinger, Ulli  and
      Sch{\"u}tze, Hinrich},
    editor = "Lapata, Mirella  and
      Blunsom, Phil  and
      Koller, Alexander",
    booktitle = "Proceedings of the 15th Conference of the {E}uropean Chapter of the Association for Computational Linguistics: Volume 2, Short Papers",
    month = apr,
    year = "2017",
    address = "Valencia, Spain",
    publisher = "Association for Computational Linguistics",
    url = "https://preview.aclanthology.org/jlcl-multiple-ingestion/E17-2119/",
    pages = "752--758",
    abstract = "We address fine-grained entity classification and propose a novel attention-based recurrent neural network (RNN) encoder-decoder that generates paths in the type hierarchy and can be trained end-to-end. We show that our model performs better on fine-grained entity classification than prior work that relies on flat or local classifiers that do not directly model hierarchical structure."
}