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In this paper we present a new method to learn a model robust to typos for aNamed Entity Recognition task. Our improvement over existing methods helps themodel to take into account the context of the sentence inside a court decisionin order to recognize an entity with a typo. We used state-of-the-art modelsand enriched the last layer of the neural network with high-level informationlinked with the potential of the word to be a certain type of entity. Moreprecisely, we utilized the similarities between the word and the potentialentity candidates in the tagged sentence context. The experiments on a datasetof French court decisions show a reduction of the relative F1-score error of32%, upgrading the score obtained with the most competitive fine-tunedstate-of-the-art system from 94.85% to 96.52%.

May I Check Again? -- A simple but efficient way to generate and use  contextual dictionaries for Named Entity Recognition. Application to French  Legal Texts