WikiNER-fr-gold: A Gold-Standard NER Corpus

TL;DR

This paper presents WikiNER-fr-gold, a revised and manually verified gold-standard French NER corpus derived from the original WikiNER, improving annotation quality and consistency for better NLP applications.

Contribution

The paper introduces a manually revised, high-quality French NER corpus, addressing the limitations of the original semi-supervised WikiNER dataset.

Findings

Identification of annotation errors and inconsistencies in WikiNER-fr

A revised corpus with improved annotation quality

Discussion of future directions for corpus enhancement

Abstract

We address in this article the the quality of the WikiNER corpus, a multilingual Named Entity Recognition corpus, and provide a consolidated version of it. The annotation of WikiNER was produced in a semi-supervised manner i.e. no manual verification has been carried out a posteriori. Such corpus is called silver-standard. In this paper we propose WikiNER-fr-gold which is a revised version of the French proportion of WikiNER. Our corpus consists of randomly sampled 20% of the original French sub-corpus (26,818 sentences with 700k tokens). We start by summarizing the entity types included in each category in order to define an annotation guideline, and then we proceed to revise the corpus. Finally we present an analysis of errors and inconsistency observed in the WikiNER-fr corpus, and we discuss potential future work directions.