<?xml version="1.0" encoding="UTF-8"?>
<modsCollection xmlns="http://www.loc.gov/mods/v3">
<mods ID="hassan-2026-precision">
    <titleInfo>
        <title>A Precision-First Whitespace Restoration Pipeline for Noisy Urdu Tafseer Text</title>
    </titleInfo>
    <name type="personal">
        <namePart type="given">Muhammad</namePart>
        <namePart type="given">Mubashir</namePart>
        <namePart type="family">Hassan</namePart>
        <role>
            <roleTerm authority="marcrelator" type="text">author</roleTerm>
        </role>
    </name>
    <originInfo>
        <dateIssued>2026-10</dateIssued>
    </originInfo>
    <typeOfResource>text</typeOfResource>
    <relatedItem type="host">
        <titleInfo>
            <title>Proceedings of the 11th Workshop on Natural User-generated Text</title>
        </titleInfo>
        <name type="personal">
            <namePart type="given">JinYeong</namePart>
            <namePart type="family">Bak</namePart>
            <role>
                <roleTerm authority="marcrelator" type="text">editor</roleTerm>
            </role>
        </name>
        <name type="personal">
            <namePart type="given">Rob</namePart>
            <namePart type="family">van der Goot</namePart>
            <role>
                <roleTerm authority="marcrelator" type="text">editor</roleTerm>
            </role>
        </name>
        <name type="personal">
            <namePart type="given">Hyeju</namePart>
            <namePart type="family">Jang</namePart>
            <role>
                <roleTerm authority="marcrelator" type="text">editor</roleTerm>
            </role>
        </name>
        <name type="personal">
            <namePart type="given">Weerayut</namePart>
            <namePart type="family">Buaphet</namePart>
            <role>
                <roleTerm authority="marcrelator" type="text">editor</roleTerm>
            </role>
        </name>
        <name type="personal">
            <namePart type="given">Alan</namePart>
            <namePart type="family">Ramponi</namePart>
            <role>
                <roleTerm authority="marcrelator" type="text">editor</roleTerm>
            </role>
        </name>
        <name type="personal">
            <namePart type="given">Wei</namePart>
            <namePart type="family">Xu</namePart>
            <role>
                <roleTerm authority="marcrelator" type="text">editor</roleTerm>
            </role>
        </name>
        <name type="personal">
            <namePart type="given">Alan</namePart>
            <namePart type="family">Ritter</namePart>
            <role>
                <roleTerm authority="marcrelator" type="text">editor</roleTerm>
            </role>
        </name>
        <originInfo>
            <publisher>Association for Computational Linguistics</publisher>
            <place>
                <placeTerm type="text">Budapest, Hungary</placeTerm>
            </place>
        </originInfo>
        <genre authority="marcgt">conference publication</genre>
        <identifier type="isbn">979-8-89176-470-5</identifier>
    </relatedItem>
    <abstract>Document conversion and import are a large but under-studied source of real-world noisy text, distinct from the social-media noise usually studied in normalization. We study missing-space artifacts in a low-resource, Arabic-script setting: an Urdu Qur’anic tafseer exported from InPage .inp to .txt and then to JSON/markdown, a pipeline that fuses adjacent words into single tokens (e.g., اورقرآن for اور قرآن, “and the Qur’an”). Restoring spaces is hard: Urdu’s cursive Arabic script does not require spaces at word boundaries, and the text interleaves Urdu prose with Qur’anic Arabic, proper names, and markdown, so a careless split can corrupt preservation-sensitive content. We cast this as precision-first normalization and present a confidence-gated pipeline that proposes only space-insertion edits, verifies a character-preservation invariant, and holds protected regions out of automatic correction. On a 1,000-row silver-labeled sample, the high-confidence auto-accept operating point reaches 96.9% strict precision on decisive cases, against 30.5% for an ungated function-word splitter and 39.7% for a frequency-only baseline, with 0 character-altering edits. This suggests that confidence gating is necessary before applying whitespace restoration to preservation-sensitive Urdu–Arabic text. As our labels are model-assisted silver rather than expert-gold, we treat the precision figures as provisional and release derived artifacts for reproducibility.</abstract>
    <identifier type="citekey">hassan-2026-precision</identifier>
    <location>
        <url>https://aclanthology.org/2026.wnut-1.1/</url>
    </location>
    <part>
        <date>2026-10</date>
        <extent unit="page">
            <start>1</start>
            <end>9</end>
        </extent>
    </part>
</mods>
</modsCollection>

%0 Conference Proceedings
%T A Precision-First Whitespace Restoration Pipeline for Noisy Urdu Tafseer Text
%A Hassan, Muhammad Mubashir
%Y Bak, JinYeong
%Y van der Goot, Rob
%Y Jang, Hyeju
%Y Buaphet, Weerayut
%Y Ramponi, Alan
%Y Xu, Wei
%Y Ritter, Alan
%S Proceedings of the 11th Workshop on Natural User-generated Text
%D 2026
%8 October
%I Association for Computational Linguistics
%C Budapest, Hungary
%@ 979-8-89176-470-5
%F hassan-2026-precision
%X Document conversion and import are a large but under-studied source of real-world noisy text, distinct from the social-media noise usually studied in normalization. We study missing-space artifacts in a low-resource, Arabic-script setting: an Urdu Qur’anic tafseer exported from InPage .inp to .txt and then to JSON/markdown, a pipeline that fuses adjacent words into single tokens (e.g., اورقرآن for اور قرآن, “and the Qur’an”). Restoring spaces is hard: Urdu’s cursive Arabic script does not require spaces at word boundaries, and the text interleaves Urdu prose with Qur’anic Arabic, proper names, and markdown, so a careless split can corrupt preservation-sensitive content. We cast this as precision-first normalization and present a confidence-gated pipeline that proposes only space-insertion edits, verifies a character-preservation invariant, and holds protected regions out of automatic correction. On a 1,000-row silver-labeled sample, the high-confidence auto-accept operating point reaches 96.9% strict precision on decisive cases, against 30.5% for an ungated function-word splitter and 39.7% for a frequency-only baseline, with 0 character-altering edits. This suggests that confidence gating is necessary before applying whitespace restoration to preservation-sensitive Urdu–Arabic text. As our labels are model-assisted silver rather than expert-gold, we treat the precision figures as provisional and release derived artifacts for reproducibility.
%U https://aclanthology.org/2026.wnut-1.1/
%P 1-9

Markdown (Informal)

[A Precision-First Whitespace Restoration Pipeline for Noisy Urdu Tafseer Text](https://aclanthology.org/2026.wnut-1.1/) (Hassan, WNUT 2026)

• A Precision-First Whitespace Restoration Pipeline for Noisy Urdu Tafseer Text (Hassan, WNUT 2026)

ACL

• Muhammad Mubashir Hassan. 2026. A Precision-First Whitespace Restoration Pipeline for Noisy Urdu Tafseer Text. In Proceedings of the 11th Workshop on Natural User-generated Text, pages 1–9, Budapest, Hungary. Association for Computational Linguistics.