@inproceedings{adel-schutze-2017-global,
    title = "Global Normalization of Convolutional Neural Networks for Joint Entity and Relation Classification",
    author = {Adel, Heike  and
      Sch{\"u}tze, Hinrich},
    editor = "Palmer, Martha  and
      Hwa, Rebecca  and
      Riedel, Sebastian",
    booktitle = "Proceedings of the 2017 Conference on Empirical Methods in Natural Language Processing",
    month = sep,
    year = "2017",
    address = "Copenhagen, Denmark",
    publisher = "Association for Computational Linguistics",
    url = "https://preview.aclanthology.org/fix-sig-urls/D17-1181/",
    doi = "10.18653/v1/D17-1181",
    pages = "1723--1729",
    abstract = "We introduce globally normalized convolutional neural networks for joint entity classification and relation extraction. In particular, we propose a way to utilize a linear-chain conditional random field output layer for predicting entity types and relations between entities at the same time. Our experiments show that global normalization outperforms a locally normalized softmax layer on a benchmark dataset."
}