AMWAL : Named Entity Recognition for A rabic Financial News (2025)

Abstract: 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.

## introduction financial markets are characterized by their volatile dynamics and constantly changing structure. price movements, trading volume, and market liquidity are all factors that contribute to this fluid environment. one of the major tools that were found to assist with the analysis and navigation of these complex financial landscapes is financial news. this type of news has been identified as instrumental in predicting stock price movements @xcite , understanding market sentiment @xcite , and informing investor decisions @xcite . additionally, the automated analysis of financial news can provide deeper insights into market dynamics, assist governments in regulating markets, and help intelligence agencies with monitoring for anomalies and unusual events @xcite . luckily, with the proliferation of online financial news platforms, we are now witnessing an abundance of textual data that is readily accessible for analysis. however, the majority of this data is unstructured, i.e., does not have a standardized format, which presents a significant challenge for effective analysis and interpretation. named entity recognition (ner) stands as one of the common approaches that aim at organizing such unstructured data into distinct categories, thereby facilitating identifying relations and patterns of interaction among these categories @xcite . even though there have been notable advances and expansions in arabic ner systems @xcite , the majority remain generic (i.e., detects entities for people, organizations, countries, etc.) rather than domain-specific, with the exception of the medical domain @xcite . this paper aims to address this gap by introducing amwal, a ner system that is designed specifically for extracting financial entities from arabic financial news articles. the remainder of this paper is organized as follows. section 2 reviews works that are pertinent to building financial ner systems. section 3 details the methodology of building amwal. in section 4, we report the system's results, and section 5 is dedicated for discussion, conclusion, and potential avenues for future research. addressing limitations in existing ner resources and the scarcity of publicly available financial corpora, @xcite developed a french corpus with a custom ontology of financial concepts. the corpus focused on entities and their relationships that are pertinent to a set of identity verification guidelines called know your customer (kyc). to build the corpus, they collected 1 million news articles from 40 daily french financial newspapers. next, they compiled a list of 130 keywords featuring company names, financial interactions, currencies, etc., which were later used to randomly select 130 articles for manual annotation. their corpus included a total of 6736 entities and 1754 relations, with varying distribution across different types. in their experiments, to test the performance of their annotated corpus in ner and relation extraction, they employed the training modules provided by spacy @xcite , which allows for custom ner training. overall, their model achieved an f1 score of 0.73. the categories of person and currency exhibited the highest accuracy and recall rates, respectively. for the task of exact relation extraction and using rule-based extraction methods, they achieved a precision score of 0.81, a recall score of 0.34, and an f1 score of 0.49. one of the issues that often pose challenges to ner systems is abbreviations due to their diverse forms and lack of clear distinguishing features. to address this @xcite , developed a model specifically designed for recognizing financial abbreviations in financial chinese news texts. their approach leveraged domain-specific knowledge and context information in a three-step process. first, stock names were extracted as initial clues for identifying potential financial entities. this was followed by the identification of internal features such as suffix keywords, geographic terms, and adjacent words. finally, they employed a combination of mutual information (mi), boundary information entropy (ie), and word similarity to identify potential abbreviations. this approach achieved 91.02 precision, 93.77 recall, and an f1 score of 0.92. with regard to the available ner systems for arabic, as mentioned above, most of the models are generic in terms of the entities they recognize. jarrar et al. (2022) ## methodology in this section, we describe the methodology of building and training the model. first, we outline the steps followed in collecting and preprocessing the data. then, we discuss the rationale guiding the selection of the financial entities. finally, we talk about the training process. ## experiment the following steps were carried out to train the model. first, all txt files were converted into a json format, which included the content of the article, the entity token (e.g., the economist) and type (e.g., media) as well as the start and end positions of each recognized entity in the text. using these json files, we randomly allocated 80% of the data (20,984 json files) for training the model and 20% (5247 json files) for testing its performance. we performed the split at the file level to maintain consistency and to ensure that no overlapping instances exist between training and testing datasets. for building and training the system, we leveraged spacy's built-in ner toolkit. spacy is an entity count corporation 6840 quantity or unit 2406 event 1417 product or service 1222 person 1193 bank 1185 metric 941 official 794 city 756 role 692 geopolitical 519 country 436 nationality 394 government entity 225 time 217 stock exchange 158 financial market 130 financial instrument 123 currency 107 media 103 total 17185 to evaluate the performance of the system over test data, we used precision, recall, and f1 scores. as amwal demonstrates superior performance in financial ner compared to existing arabic ner models. as shown in tables 2 and 3, amwal outperforms camelbert msa ner @xcite and wojood flatner @xcite in financial ner tasks. this improvement can be due to amwal's broader set of entities and labels being specifically targeted towards the financial domain. in contrast, camelbert and wojood are regarded as general-purpose ner models and are less specific to the financial domain. ## error analysis evaluating our amwal system revealed several insights regarding its performance and limitations. despite achieving high evaluation scores overall, specific challenges persist in the system's handling of certain entity categories, e.g., corporation and person. we noticed that in corporation for instance many of the entities were not labeled correctly because several company names included categories that overlap with other categories we have such as products or services (e.g., euromed for medical industries), nationalities (e.g., wind italy), or even temporal references. for example, nissan shares the same spelling as the word for the month of april in levantine arabic " ". the same issue persists with the person category, where some individuals' names include nationalities, such as " (the swedish). amwal's excellent performance also hinges on it being good at tagging seen data, which might be seen as overfitting; however, even general-purpose ner models fail at such unseen data. thus, further analysis on training with more diverse and domain-specific data could enhance amwal's ability to generalize to unseen instances. also, incorporating strategies such as expanding the training dataset to include more examples of overlapping or ambiguous categories, applying data augmentation techniques, and fine- ## limitations due to the nature of this task, i.e., recognizing entities in financial news, amwal may not be able to generalize over different variations of arabic other than msa, which means that this may limit the model's ability to generalize over other financial sources such as blogs or social media posts. ## conclusion in this paper, we described the development of amwal, the first arabic financial named entity recognition system. to build the model, we first created a corpus from three major arabic financial newspapers and then used a twofold semiautomated approach to extract entities from the corpus, which we believe is adaptable to other languages that exhibit similar linguistic patterns. further, in order to avoid arbitrary or subjective choices in selecting the entity types, we adopted financial entities from the financial industry business ontology (fibo). we trained the model using spacy's custom ner pipeline and employed arabert large for processing the data. the evaluation results of the model on the test data showed strong performance metrics with precision at 96.08%, recall at 95.87%, and f1-score at 95.97%, outperforming financial ner systems in other languages as well as general-purpose arabic ner systems. for future directions, we consider the following steps. first, we aim to expand the size of the corpus as well as the number of entity types. this entails restructuring the identified entities into more intricate hierarchical structures. additionally, we are considering expanding the scope of the model to encompass not only entity types but also their interrelations, with the ultimate objective being building an arabic financial knowledge graph that can better inform various stakeholders in the field of finance.