  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 court 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 in the tagged sentence context. The experiments on a dataset
of French court 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%.
