@inproceedings{zaratiana-etal-2022-global,
    title = "Global Span Selection for Named Entity Recognition",
    author = "Zaratiana, Urchade  and
      Elkhbir, Niama  and
      Holat, Pierre  and
      Tomeh, Nadi  and
      Charnois, Thierry",
    booktitle = "Proceedings of the Workshop on Unimodal and Multimodal Induction of Linguistic Structures (UM-IoS)",
    month = dec,
    year = "2022",
    address = "Abu Dhabi, United Arab Emirates (Hybrid)",
    publisher = "Association for Computational Linguistics",
    url = "https://aclanthology.org/2022.umios-1.2",
    pages = "11--17",
    abstract = "Named Entity Recognition (NER) is an important task in Natural Language Processing with applications in many domains. In this paper, we describe a novel approach to named entity recognition, in which we output a set of spans (i.e., segmentations) by maximizing a global score. During training, we optimize our model by maximizing the probability of the gold segmentation. During inference, we use dynamic programming to select the best segmentation under a linear time complexity. We prove that our approach outperforms CRF and semi-CRF models for Named Entity Recognition. We will make our code publicly available.",
}