Guidelines and Annotation Framework for Arabic Author Profiling
This work addresses the need for high-quality datasets in Arabic NLP, particularly for dialectal analysis, but is incremental as it focuses on annotation processes rather than novel methods.
The authors tackled the problem of creating a large annotated dataset for Arabic author profiling by developing guidelines and an annotation framework for social media text, resulting in the ARAP-Tweet corpus with over 2.4 million words covering 16 countries and 11 dialectal regions.
In this paper, we present the annotation pipeline and the guidelines we wrote as part of an effort to create a large manually annotated Arabic author profiling dataset from various social media sources covering 16 Arabic countries and 11 dialectal regions. The target size of the annotated ARAP-Tweet corpus is more than 2.4 million words. We illustrate and summarize our general and dialect-specific guidelines for each of the dialectal regions selected. We also present the annotation framework and logistics. We control the annotation quality frequently by computing the inter-annotator agreement during the annotation process. Finally, we describe the issues encountered during the annotation phase, especially those related to the peculiarities of Arabic dialectal varieties as used in social media.