Financial Named Entity Recognition (NER) presents a pivotal task in extracting structured information from unstructured financial data, especially when extending its application to languages beyond English. In this paper, we present AMWAL, a named entity recognition system for Arabic financial news. Our approach centered on building a specialized corpus compiled from three major Arabic financial newspapers spanning from 2000 to 2023. Entities were extracted from this corpus using a semi-automatic process that included manual annotation and review to ensure accuracy. The total number of entities identified amounts to 17.1k tokens, distributed across 20 categories, providing a comprehensive coverage of financial entities. To standardize the identified entities, we adopt financial concepts from the Financial Industry Business Ontology (FIBO, 2020), aligning our framework with industry standards. The significance of our work lies not only in the creation of the first customized NER system for Arabic financial data but also in its potential to streamline information extraction processes in the financial domain. Our NER system achieves a Precision score of 96.08, a Recall score of 95.87, and an F1 score of 95.97, which outperforms state-of-the-art general Arabic NER systems as well as other systems for financial NER in other languages.

In this paper, we describe our participation in the NADI2023 shared task for the classification of Arabic dialects in tweets. For training, evaluation, and testing purposes, a primary dataset comprising tweets from 18 Arab countries is provided, along with three older datasets. The main objective is to develop a model capable of classifying tweets from these 18 countries. We outline our approach, which leverages various machine learning models. Our experiments demonstrate that large language models, particularly Arabertv2-Large, Arabertv2-Base, and CAMeLBERT-Mix DID MADAR, consistently outperform traditional methods such as SVM, XGBOOST, Multinomial Naive Bayes, AdaBoost, and Random Forests.