Enriching the NArabizi Treebank: A Multifaceted Approach to Supporting an Under-Resourced Language

TL;DR

This paper enhances the NArabizi Treebank by adding new annotation layers and re-annotating existing layers, improving NLP research for this under-resourced language and demonstrating the importance of tokenization choices.

Contribution

It introduces an enriched NArabizi Treebank with new annotation layers and re-annotations, facilitating advanced NLP research for NArabizi.

Findings

Improved annotation consistency and quality.

Demonstrated the impact of tokenization schemes on NLP tasks.

Public release of the enriched dataset.

Abstract

In this paper we address the scarcity of annotated data for NArabizi, a Romanized form of North African Arabic used mostly on social media, which poses challenges for Natural Language Processing (NLP). We introduce an enriched version of NArabizi Treebank (Seddah et al., 2020) with three main contributions: the addition of two novel annotation layers (named entity recognition and offensive language detection) and a re-annotation of the tokenization, morpho-syntactic and syntactic layers that ensure annotation consistency. Our experimental results, using different tokenization schemes, showcase the value of our contributions and highlight the impact of working with non-gold tokenization for NER and dependency parsing. To facilitate future research, we make these annotations publicly available. Our enhanced NArabizi Treebank paves the way for creating sophisticated language models and NLP tools for this under-represented language.