Etiqueter un corpus oral par apprentissage automatique \`a l'aide de connaissances linguistiques

TL;DR

This paper presents a machine learning approach using hierarchical linguistic knowledge to automatically label an oral corpus with morpho-syntactic tags, achieving high accuracy despite the data's oral nature.

Contribution

It introduces a new hierarchical label structure and a tailored CRF-based labeling tool for oral data, integrating linguistic expertise.

Findings

Achieved 85-90% labeling accuracy.

Developed a new hierarchical label set.

Built a specialized labeling tool for oral corpora.

Abstract

Thanks to the Eslo1 ("Enqu\^ete sociolinguistique d'Orl\'eans", i.e. "Sociolinguistic Inquiery of Orl\'eans") campain, a large oral corpus has been gathered and transcribed in a textual format. The purpose of the work presented here is to associate a morpho-syntactic label to each unit of this corpus. To this aim, we have first studied the specificities of the necessary labels, and their various possible levels of description. This study has led to a new original hierarchical structuration of labels. Then, considering that our new set of labels was different from the one used in every available software, and that these softwares usually do not fit for oral data, we have built a new labeling tool by a Machine Learning approach, from data labeled by Cordial and corrected by hand. We have applied linear CRF (Conditional Random Fields) trying to take the best possible advantage of the linguistic knowledge that was used to define the set of labels. We obtain an accuracy between 85 and 90%, depending of the parameters used.