An Empirical Analysis of Task Relations in the Multi-Task Annotation of an Arabizi Corpus - Archive ouverte HAL Accéder directement au contenu
Communication Dans Un Congrès Année : 2023

An Empirical Analysis of Task Relations in the Multi-Task Annotation of an Arabizi Corpus

Résumé

In this study, we deal with the design of computational-linguistic resources and strategies for the analysis of under-resourced languages. In particular, we present empirical analyses aiming at identifying the best path to semi-automatically annotate a dialectal Arabic corpus via a neural multi-task architecture. Such an architecture is used to automatically generate several levels of linguistic annotation which can be evaluated by comparison with the gold annotation. Changing the order in which annotations are produced can have an impact on the quantitative results. Through multiple sets of experiments we show how to get the best performances with this methodology.
Fichier principal
Vignette du fichier
2023.ldk-1.14.pdf (346.3 Ko) Télécharger le fichier
Origine : Fichiers éditeurs autorisés sur une archive ouverte

Dates et versions

hal-04299027 , version 1 (21-11-2023)

Identifiants

  • HAL Id : hal-04299027 , version 1

Citer

Elisa Gugliotta, Marco Dinarelli. An Empirical Analysis of Task Relations in the Multi-Task Annotation of an Arabizi Corpus. Conference on Language, Data and Knowledge, Sep 2023, Vienna, Austria. ⟨hal-04299027⟩
26 Consultations
4 Téléchargements

Partager

Gmail Facebook X LinkedIn More