aiqbench analysis
This paper presents a methodological framework for Arabic financial natural language processing, focusing on the integration of linguistic context and documentary provenance in analyzing corporate reports and market disclosures. It addresses specific challenges in this domain, including vocabulary analysis, entity recognition, causal attribution, and the impact of dialectal intent detection on institutional communication. The work contributes to the broader field by establishing evaluation benchmarks and pretraining corpora tailored for Arabic financial text, ensuring that computational representations remain grounded in their source documents.
Arabic Financial NLP
arXiv:2610.25854v1 Announce Type: new Abstract: Financial communication connects numerical accounts of economic activity with explanations of performance, responsibility and future expectations. Arabic financial natural language processing offers methods for studying these connections across corporate reports, market disclosures, journalism and banking interactions. Its contribution to digital humanities depends on retaining the relationship between a computational representation and the document from which it was made. This chapter follows that relationship from source collection and Arabic text processing to the analysis of vocabulary, evaluation, entities, causal attribution and multilingual reporting. It then examines what financial summaries and retrieval-grounded answers preserve or lose, and how dialectal intent detection changes the perspective from institutions speaking to institutions listening. Published Arabic resources are discussed in relation to the questions they were designed to answer, with a distinction between financial pretraining corpora, annotated datasets and evaluation benchmarks. A running constructed example connects the methodological discussion without presenting illustrative analysis as empirical findings. The chapter argues for an account of Arabic financial NLP in which linguistic context, documentary provenance and interpretive judgement remain part of the evidence throughout the analytical process.
Abstract:Financial communication connects numerical accounts of economic activity with explanations of performance, responsibility and future expectations. Arabic financial natural language processing offers methods for studying these connections across corporate reports, market disclosures, journalism and banking interactions. Its contribution to digital humanities depends on retaining the relationship between a computational representation and the document from which it was made. This chapter follows that relationship from source collection and Arabic text processing to the analysis of vocabulary, evaluation, entities, causal attribution and multilingual reporting. It then examines what financial summaries and retrieval-grounded answers preserve or lose, and how dialectal intent detection changes the perspective from institutions speaking to institutions listening. Published Arabic resources are discussed in relation to the questions they were designed to answer, with a distinction between financial pretraining corpora, annotated datasets and evaluation benchmarks. A running constructed example connects the methodological discussion without presenting illustrative analysis as empirical findings. The chapter argues for an account of Arabic financial NLP in which linguistic context, documentary provenance and interpretive judgement remain part of the evidence throughout the analytical process.
Computation and Language (cs.CL)
arXiv:2610.25854 [cs.CL]
(or arXiv:2610.25854v1 [cs.CL] for this version)
https://doi.org/10.48550/arXiv.2610.25854 Focus to learn more arXiv-issued DOI via DataCite
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