WikiNER-fr-gold: A Gold-Standard NER Corpus
This work provides a high-quality resource for French Named Entity Recognition, addressing data reliability for researchers and practitioners in NLP, though it is incremental as it refines an existing dataset.
The authors tackled the quality issues of the French portion of the WikiNER corpus by creating WikiNER-fr-gold, a revised gold-standard version, which involved manually verifying 20% of the original data (26,818 sentences with 700k tokens) and analyzing errors and inconsistencies.
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.