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

TL;DR

This paper introduces a robust method for Named Entity Recognition in French legal texts that leverages contextual dictionaries and high-level neural network features to effectively handle typos, significantly improving accuracy.

Contribution

It presents a novel approach combining contextual dictionaries and high-level neural features to enhance NER robustness to typos in legal French texts.

Findings

32% reduction in F1-score error

F1-score improved from 94.85% to 96.52%

Effective handling of typos in legal NER

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 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%.