@inproceedings{faqihi-etal-2026-qamar,
title = "{QAMAR}: A New Fully Verified and Accurate {Q}uranic {A}rabic Morphological Analysis Resource.",
author = "Faqihi, Sara and
Bouzoubaa, Karim and
Tajmout, Rachida and
Namly, Driss",
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-1.38/",
pages = "301--312",
abstract = "Several Quranic morphological corpora have been developed to support Arabic linguistic analysis and NLP applications, yet they often lack full coverage, consistency, or manual verification. We present QAMAR, a morphologically oriented, multi-task corpus derived from the Qur{'}an. This comprehensive, manually verified resource provides a detailed linguistic layer for every Quranic word, including the Modern Standard Arabic (MSA) equivalent, the stem, the lemma, the root, and the part of speech (POS). QAMAR supports multiple NLP tasks, such as normalization, lemmatization, root extraction, and POS tagging, and serves as a gold-standard reference for Quranic and Arabic NLP research, including corpus-to-corpus evaluation and morphological analyzer benchmarking. The paper details QAMAR{'}s annotation framework, verification process, and resource structure, and reports comparative analyses with existing Quranic morphological resources and outputs produced by current large language models (LLMs)."
}<?xml version="1.0" encoding="UTF-8"?>
<modsCollection xmlns="http://www.loc.gov/mods/v3">
<mods ID="faqihi-etal-2026-qamar">
<titleInfo>
<title>QAMAR: A New Fully Verified and Accurate Quranic Arabic Morphological Analysis Resource.</title>
</titleInfo>
<name type="personal">
<namePart type="given">Sara</namePart>
<namePart type="family">Faqihi</namePart>
<role>
<roleTerm authority="marcrelator" type="text">author</roleTerm>
</role>
</name>
<name type="personal">
<namePart type="given">Karim</namePart>
<namePart type="family">Bouzoubaa</namePart>
<role>
<roleTerm authority="marcrelator" type="text">author</roleTerm>
</role>
</name>
<name type="personal">
<namePart type="given">Rachida</namePart>
<namePart type="family">Tajmout</namePart>
<role>
<roleTerm authority="marcrelator" type="text">author</roleTerm>
</role>
</name>
<name type="personal">
<namePart type="given">Driss</namePart>
<namePart type="family">Namly</namePart>
<role>
<roleTerm authority="marcrelator" type="text">author</roleTerm>
</role>
</name>
<originInfo>
<dateIssued>2026-03</dateIssued>
</originInfo>
<typeOfResource>text</typeOfResource>
<relatedItem type="host">
<titleInfo>
<title>Proceedings of the 2nd Workshop on NLP for Languages Using Arabic Script</title>
</titleInfo>
<originInfo>
<publisher>Association for Computational Linguistics</publisher>
<place>
<placeTerm type="text">Rabat, Morocco</placeTerm>
</place>
</originInfo>
<genre authority="marcgt">conference publication</genre>
</relatedItem>
<abstract>Several Quranic morphological corpora have been developed to support Arabic linguistic analysis and NLP applications, yet they often lack full coverage, consistency, or manual verification. We present QAMAR, a morphologically oriented, multi-task corpus derived from the Qur’an. This comprehensive, manually verified resource provides a detailed linguistic layer for every Quranic word, including the Modern Standard Arabic (MSA) equivalent, the stem, the lemma, the root, and the part of speech (POS). QAMAR supports multiple NLP tasks, such as normalization, lemmatization, root extraction, and POS tagging, and serves as a gold-standard reference for Quranic and Arabic NLP research, including corpus-to-corpus evaluation and morphological analyzer benchmarking. The paper details QAMAR’s annotation framework, verification process, and resource structure, and reports comparative analyses with existing Quranic morphological resources and outputs produced by current large language models (LLMs).</abstract>
<identifier type="citekey">faqihi-etal-2026-qamar</identifier>
<location>
<url>https://aclanthology.org/2026.abjadnlp-1.38/</url>
</location>
<part>
<date>2026-03</date>
<extent unit="page">
<start>301</start>
<end>312</end>
</extent>
</part>
</mods>
</modsCollection>
%0 Conference Proceedings
%T QAMAR: A New Fully Verified and Accurate Quranic Arabic Morphological Analysis Resource.
%A Faqihi, Sara
%A Bouzoubaa, Karim
%A Tajmout, Rachida
%A Namly, Driss
%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 faqihi-etal-2026-qamar
%X Several Quranic morphological corpora have been developed to support Arabic linguistic analysis and NLP applications, yet they often lack full coverage, consistency, or manual verification. We present QAMAR, a morphologically oriented, multi-task corpus derived from the Qur’an. This comprehensive, manually verified resource provides a detailed linguistic layer for every Quranic word, including the Modern Standard Arabic (MSA) equivalent, the stem, the lemma, the root, and the part of speech (POS). QAMAR supports multiple NLP tasks, such as normalization, lemmatization, root extraction, and POS tagging, and serves as a gold-standard reference for Quranic and Arabic NLP research, including corpus-to-corpus evaluation and morphological analyzer benchmarking. The paper details QAMAR’s annotation framework, verification process, and resource structure, and reports comparative analyses with existing Quranic morphological resources and outputs produced by current large language models (LLMs).
%U https://aclanthology.org/2026.abjadnlp-1.38/
%P 301-312
Markdown (Informal)
[QAMAR: A New Fully Verified and Accurate Quranic Arabic Morphological Analysis Resource.](https://aclanthology.org/2026.abjadnlp-1.38/) (Faqihi et al., AbjadNLP 2026)
ACL