@inproceedings{younes-2026-deformar,
title = "{D}eform{AR}: A Visual Analytics Framework for Evaluation of {A}rabic Named Entity Recognition",
author = "Younes, Ahmed Mustafa",
booktitle = "Proceedings of the 2nd Workshop on {NLP} for Languages Using {A}rabic Script",
month = mar,
year = "2026",
address = "Rabat, Morocco",
publisher = "Association for Computational Linguistics",
url = "https://aclanthology.org/2026.abjadnlp-1.34/",
pages = "253--275",
abstract = "Arabic Named Entity Recognition (ANER) presents challenges due to its linguistic characteristics (Qu et al., 2023). While Transformer models have advanced ANER, evaluation still relies heavily on aggregate metrics like F1 score that obscure the interplay between data characteristics, model behaviour, and error patterns. We present DeformAR, a diagnostic visual analytics framework for evaluating and diagnosing Arabic NER systems through structured, component-level analysis and interpretability. DeformAR integrates quantitative metrics with interactive visualizations to support systematic error analysis, dataset and model debugging. In a case study on ANERCorp, DeformAR identifies annotation mistakes, model calibration issues, and subcomponent interaction effects. To our knowledge, this is the first open-source framework for component-level diagnostic evaluation and interpretability in Arabic NER, available at https://github.com/ay94/DeformAR."
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%0 Conference Proceedings
%T DeformAR: A Visual Analytics Framework for Evaluation of Arabic Named Entity Recognition
%A Younes, Ahmed Mustafa
%S Proceedings of the 2nd Workshop on NLP for Languages Using Arabic Script
%D 2026
%8 March
%I Association for Computational Linguistics
%C Rabat, Morocco
%F younes-2026-deformar
%X Arabic Named Entity Recognition (ANER) presents challenges due to its linguistic characteristics (Qu et al., 2023). While Transformer models have advanced ANER, evaluation still relies heavily on aggregate metrics like F1 score that obscure the interplay between data characteristics, model behaviour, and error patterns. We present DeformAR, a diagnostic visual analytics framework for evaluating and diagnosing Arabic NER systems through structured, component-level analysis and interpretability. DeformAR integrates quantitative metrics with interactive visualizations to support systematic error analysis, dataset and model debugging. In a case study on ANERCorp, DeformAR identifies annotation mistakes, model calibration issues, and subcomponent interaction effects. To our knowledge, this is the first open-source framework for component-level diagnostic evaluation and interpretability in Arabic NER, available at https://github.com/ay94/DeformAR.
%U https://aclanthology.org/2026.abjadnlp-1.34/
%P 253-275
Markdown (Informal)
[DeformAR: A Visual Analytics Framework for Evaluation of Arabic Named Entity Recognition](https://aclanthology.org/2026.abjadnlp-1.34/) (Younes, AbjadNLP 2026)
ACL