Takween: A Comprehensive Arabic Collection and Annotation Tool
Iyad Elwy, Caroline Sabty · Procedia Computer Science · 2024
In the rapidly evolving field of Natural Language Processing (NLP), the availability of labeled data is crucial for developing and improving machine learning models. Labeled data provides the essential context and structure required for training algorithms to accurately interpret and process human language. However, obtaining labeled data, especially for languages with complex structures like Arabic, presents significant challenges. This paper introduces ”Takween,” a novel tool designed to meet the need for high-quality labeled data in Arabic NLP. Takween leverages social media and Wikipedia as data sources to facilitate the collection of large-scale Arabic text data. The tool enables users to annotate and label the collected data, with functionalities for reviewing annotations and visualizing the labeled data to ensure quality. Additionally, Takween incorporates active learning techniques, which allow for automatically selecting data samples for annotation based on model uncertainty. By integrating data collection, annotation, review, and visualization features with active learning capabilities, Takween offers a comprehensive solution for acquiring high-quality labeled data for Arabic NLP tasks.