---
title: "DeformAR — paper by Younes (2026) — summary and ideas"
description: "The author presents DeformAR, an open-source visual analytics framework that splits an NER system… 17 references, cited by 1 paper here."
authors: ["Ahmed Mustafa Younes"]
year: 2026
venue: "Proceedings of the 2nd Workshop on NLP for Languages Using Arabic Script"
source: "https://aclanthology.org/2026.abjadnlp-1.34/"
doi: "10.18653/v1/2026.abjadnlp-1.34"
licence: "CC BY 4.0 (ACL Anthology)"
licence_url: "https://creativecommons.org/licenses/by/4.0/"
summary_licence: "Elamur Cosmic License"
canonical: "https://research.elamur.ai/free/en/papers/younes-2026-deformar-visual-analytics-framework-evaluation/"
---
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# DeformAR: A Visual Analytics Framework for Evaluation of Arabic Named Entity Recognition

**Authors:** [Ahmed Mustafa Younes](https://md.research.elamur.ai/free/en/authors/ahmed-mustafa-younes/) · **Year:** 2026 · **Venue:** [Proceedings of the 2nd Workshop on NLP for Languages Using Arabic Script](https://md.research.elamur.ai/free/en/venues/abjadnlp/) ([Workshop on NLP for Languages Using Arabic Script 2026](https://md.research.elamur.ai/free/en/venues/abjadnlp/2026/)) · **Publisher:** [Association for Computational Linguistics](https://md.research.elamur.ai/free/en/publishers/association-for-computational-linguistics/) · **Licence:** [CC BY 4.0 (ACL Anthology)](https://creativecommons.org/licenses/by/4.0/)

**Links:** [Publisher's page](https://aclanthology.org/2026.abjadnlp-1.34/) · [PDF](https://aclanthology.org/2026.abjadnlp-1.34.pdf) · [DOI 10.18653/v1/2026.abjadnlp-1.34](https://doi.org/10.18653/v1/2026.abjadnlp-1.34) · [OpenAlex](https://openalex.org/W7140102257)

**Citations:** 1 citations (OpenAlex, 2026-10-14) · [1](https://md.research.elamur.ai/free/en/papers/younes-2026-deformar-visual-analytics-framework-evaluation/cited-by/) in our library, of the 149,439 papers whose bibliographies we read

## Summary

Standard evaluation of Arabic named entity recognition reports precision, recall and F1. These single numbers hide how data properties, learned representations and prediction behaviour jointly cause errors, and existing interpretability tools mostly target English or classification tasks. The author presents DeformAR, an open-source visual analytics framework that splits an NER system into data, model and evaluation subcomponents. Each subcomponent is described with metrics at several granularities, from corpus statistics down to per-token ambiguity, confidence, loss and embedding separability. An extraction phase fine-tunes a configurable pipeline and computes these metrics. A dashboard phase then connects them through linked interactive views, so users can drill down from system-level comparison to individual sentences. Applied to ANERCorp with a fine-tuned AraBERTv02 model, the tool exposes labelling errors, poorly calibrated predictions and side effects of subword tokenisation that aggregate scores do not reveal. This matters because it turns evaluation into diagnosis and helps decide whether to fix the data, the preprocessing or the model. The methodology is described as language- and model-agnostic.

## Key points

- **Case study data:** ANERCorp (Benajiba et al., 2007, in the CAMeL Lab standardised release) has about 150K words of Modern Standard Arabic tagged with PER, ORG, LOC and MISC in IOB2. It uses a sequential 5:1 split:
  - train: 125,102 words, of which 13,181 are NE words;
  - test: 25,008 words, of which 3,375 are NE words.
- **Setup:** The model is AraBERTv02-base with a linear classification head, trained with AdamW (lr 5×10⁻⁵, batch size 16) for 4 epochs. With WordPiece, only the first subword of each word ("core token") gets a label and contributes to the loss, and the framework analyses how this shapes behaviour.
- **Per-entity results (precision/recall):**
  - LOC: 0.893/0.934
  - PER: 0.860/0.844
  - ORG: 0.784/0.751
  - MISC: 0.772/0.634, the weakest and least frequent class (235 test examples)
- **Dashboard and evaluation tools:** The dashboard has three views:
  - a Cross-Component View;
  - a Behavioural Analysis View, in which brushing and filtering over as many as six variables connect a UMAP projection, a scatter plot of model behaviour and a heatmap showing correlations between metrics;
  - an Instance-Level View, which offers a sentence viewer for IOB1/IOB2, token analysis and BERTviz attention comparison before and after fine-tuning.

  In addition, seqeval is extended to separate boundary, exclusion and type errors at span level.
- **Availability and limitations:** The code is at github.com/ay94/DeformAR. The framework diagnoses but does not fix errors, ignores training dynamics and covers only one dataset and model configuration. Interpreting its findings also requires Arabic linguistic expertise.

*Our summary of the paper, in our own words — not its text. The work belongs to its authors; read it at the links above.*

## Institutions

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## Cite

Cite as: Ahmed Mustafa Younes. 2026. DeformAR: A Visual Analytics Framework for Evaluation of Arabic Named Entity Recognition. Proceedings of the 2nd Workshop on NLP for Languages Using Arabic Script.

The paper itself, not this page. BibTeX from the library's record of the work.

```bibtex
@inproceedings{younes2026deformar,
  title = {{DeformAR: A Visual Analytics Framework for Evaluation of Arabic Named Entity Recognition}},
  author = {Ahmed Mustafa Younes},
  booktitle = {Proceedings of the 2nd Workshop on NLP for Languages Using Arabic Script},
  year = {2026},
  doi = {10.18653/v1/2026.abjadnlp-1.34},
  url = {https://doi.org/10.18653/v1/2026.abjadnlp-1.34}
}
```

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## References

17 references, 4 of them with a card of ours. ↗ leads to the work, «find» searches for one we have no id for yet.

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*Updated 1 November 2026*