Valentin Barriere
Cour de Cassation, Palais de Justice, 5 quai de l’horloge, 75001 Paris, France
Amaury Fouret
Cour de Cassation, Palais de Justice, 5 quai de l’horloge, 75001 Paris, France
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Published in: Proceedings of the 22nd Nordic Conference on Computational Linguistics (NoDaLiDa), September 30 - October 2, Turku, Finland
Linköping Electronic Conference Proceedings 167:36, p. 327--332
NEALT Proceedings Series 42:36, p. 327--332
Published: 2019-10-02
ISBN: 978-91-7929-995-8
ISSN: 1650-3686 (print), 1650-3740 (online)
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\%.
