@inproceedings{barriere-fouret-2019-may,
    title = "May {I} Check Again? {---} A simple but efficient way to generate and use contextual dictionaries for Named Entity Recognition. Application to {F}rench Legal Texts.",
    author = "Barriere, Valentin  and
      Fouret, Amaury",
    editor = "Hartmann, Mareike  and
      Plank, Barbara",
    booktitle = "Proceedings of the 22nd Nordic Conference on Computational Linguistics",
    month = sep # "–" # oct,
    year = "2019",
    address = "Turku, Finland",
    publisher = {Link{\"o}ping University Electronic Press},
    url = "https://preview.aclanthology.org/jlcl-multiple-ingestion/W19-6136/",
    pages = "327--332",
    abstract = "In this paper we present a new method to learn a model robust to typos for a Named Entity Recognition task. Our improvement over existing methods helps the model to take into account the context of the sentence inside a justice decision in order to recognize an entity with a typo. We used state-of-the-art models and enriched the last layer of the neural network with high-level information linked with the potential of the word to be a certain type of entity. More precisely, we utilized the similarities between the word and the potential entity candidates the tagged sentence context. The experiments on a dataset of french justice decisions show a reduction of the relative F1-score error of 32{\%}, upgrading the score obtained with the most competitive fine-tuned state-of-the-art system from 94.85{\%} to 96.52{\%}."
}