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

Valentin Barriere, Amaury Fouret

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%.
Anthology ID:
W19-6136
Volume:
Proceedings of the 22nd Nordic Conference on Computational Linguistics
Month:
September–October
Year:
2019
Address:
Turku, Finland
Editors:
Mareike Hartmann, Barbara Plank
Venue:
NoDaLiDa
SIG:
Publisher:
Linköping University Electronic Press
Note:
Pages:
327–332
Language:
URL:
https://aclanthology.org/W19-6136
DOI:
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Cite (ACL):
Valentin Barriere and Amaury Fouret. 2019. May I Check Again? — A simple but efficient way to generate and use contextual dictionaries for Named Entity Recognition. Application to French Legal Texts.. In Proceedings of the 22nd Nordic Conference on Computational Linguistics, pages 327–332, Turku, Finland. Linköping University Electronic Press.
Cite (Informal):
May I Check Again? — A simple but efficient way to generate and use contextual dictionaries for Named Entity Recognition. Application to French Legal Texts. (Barriere & Fouret, NoDaLiDa 2019)
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