@inproceedings{razzaghi-etal-2026-hplt3,
title = "{HPLT}3-{P}er{R}ef: A Refined Large-Scale {P}ersian Web Corpus via Supervised Document Quality Filtering",
author = "Razzaghi, Alireza and
Galica, Tomasz and
Henriksson, Erik and
Laippala, Veronika",
editor = "Ortiz Suarez, Pedro and
Burchell, Laurie and
Ljube{\v{s}}i{\'c}, Nikola and
Scherrer, Yves and
Laippala, Veronika and
Dini{\'c}, Vuk and
Vaughan, Thom",
booktitle = "Proceedings of the 13th Web-as-Corpus Workshop",
month = oct,
year = "2026",
address = "Budapest, Hungary",
publisher = "Association for Computational Linguistics",
url = "https://aclanthology.org/2026.wac-1.6/",
pages = "60--69",
ISBN = "979-8-89176-481-1",
abstract = "Large-scale web corpora are valuable resources for natural language processing (NLP), linguistic research, and language model training, particularly for digitally low-resource languages such as Persian. However, web-derived text often contains document-level noise, even after quality-filtering pipelines. We present HPLT3 Persian Refined (HPLT3-PerRef), a refined Persian web corpus containing 105.9 billion tokens and 102.37 million documents, obtained by applying a supervised quality-filtering pipeline to the Persian subset of HPLT3. We manually annotated 6,144 Persian web documents and trained a multilingual transformer-based classifier to distinguish high- and low-quality documents. The classifier removed approximately 20{\%} of the original corpus while preserving broad domain coverage and complements HPLT3{'}s heuristic-based Web Document Score (WDS) by identifying additional low-quality documents. We publicly release the corpus, the trained classifier, and the complete source code."
}<?xml version="1.0" encoding="UTF-8"?>
<modsCollection xmlns="http://www.loc.gov/mods/v3">
<mods ID="razzaghi-etal-2026-hplt3">
<titleInfo>
<title>HPLT3-PerRef: A Refined Large-Scale Persian Web Corpus via Supervised Document Quality Filtering</title>
</titleInfo>
<name type="personal">
<namePart type="given">Alireza</namePart>
<namePart type="family">Razzaghi</namePart>
<role>
<roleTerm authority="marcrelator" type="text">author</roleTerm>
</role>
</name>
<name type="personal">
<namePart type="given">Tomasz</namePart>
<namePart type="family">Galica</namePart>
<role>
<roleTerm authority="marcrelator" type="text">author</roleTerm>
</role>
</name>
<name type="personal">
<namePart type="given">Erik</namePart>
<namePart type="family">Henriksson</namePart>
<role>
<roleTerm authority="marcrelator" type="text">author</roleTerm>
</role>
</name>
<name type="personal">
<namePart type="given">Veronika</namePart>
<namePart type="family">Laippala</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 13th Web-as-Corpus Workshop</title>
</titleInfo>
<name type="personal">
<namePart type="given">Pedro</namePart>
<namePart type="family">Ortiz Suarez</namePart>
<role>
<roleTerm authority="marcrelator" type="text">editor</roleTerm>
</role>
</name>
<name type="personal">
<namePart type="given">Laurie</namePart>
<namePart type="family">Burchell</namePart>
<role>
<roleTerm authority="marcrelator" type="text">editor</roleTerm>
</role>
</name>
<name type="personal">
<namePart type="given">Nikola</namePart>
<namePart type="family">Ljubešić</namePart>
<role>
<roleTerm authority="marcrelator" type="text">editor</roleTerm>
</role>
</name>
<name type="personal">
<namePart type="given">Yves</namePart>
<namePart type="family">Scherrer</namePart>
<role>
<roleTerm authority="marcrelator" type="text">editor</roleTerm>
</role>
</name>
<name type="personal">
<namePart type="given">Veronika</namePart>
<namePart type="family">Laippala</namePart>
<role>
<roleTerm authority="marcrelator" type="text">editor</roleTerm>
</role>
</name>
<name type="personal">
<namePart type="given">Vuk</namePart>
<namePart type="family">Dinić</namePart>
<role>
<roleTerm authority="marcrelator" type="text">editor</roleTerm>
</role>
</name>
<name type="personal">
<namePart type="given">Thom</namePart>
<namePart type="family">Vaughan</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">979-8-89176-481-1</identifier>
</relatedItem>
<abstract>Large-scale web corpora are valuable resources for natural language processing (NLP), linguistic research, and language model training, particularly for digitally low-resource languages such as Persian. However, web-derived text often contains document-level noise, even after quality-filtering pipelines. We present HPLT3 Persian Refined (HPLT3-PerRef), a refined Persian web corpus containing 105.9 billion tokens and 102.37 million documents, obtained by applying a supervised quality-filtering pipeline to the Persian subset of HPLT3. We manually annotated 6,144 Persian web documents and trained a multilingual transformer-based classifier to distinguish high- and low-quality documents. The classifier removed approximately 20% of the original corpus while preserving broad domain coverage and complements HPLT3’s heuristic-based Web Document Score (WDS) by identifying additional low-quality documents. We publicly release the corpus, the trained classifier, and the complete source code.</abstract>
<identifier type="citekey">razzaghi-etal-2026-hplt3</identifier>
<location>
<url>https://aclanthology.org/2026.wac-1.6/</url>
</location>
<part>
<date>2026-10</date>
<extent unit="page">
<start>60</start>
<end>69</end>
</extent>
</part>
</mods>
</modsCollection>
%0 Conference Proceedings
%T HPLT3-PerRef: A Refined Large-Scale Persian Web Corpus via Supervised Document Quality Filtering
%A Razzaghi, Alireza
%A Galica, Tomasz
%A Henriksson, Erik
%A Laippala, Veronika
%Y Ortiz Suarez, Pedro
%Y Burchell, Laurie
%Y Ljubešić, Nikola
%Y Scherrer, Yves
%Y Laippala, Veronika
%Y Dinić, Vuk
%Y Vaughan, Thom
%S Proceedings of the 13th Web-as-Corpus Workshop
%D 2026
%8 October
%I Association for Computational Linguistics
%C Budapest, Hungary
%@ 979-8-89176-481-1
%F razzaghi-etal-2026-hplt3
%X Large-scale web corpora are valuable resources for natural language processing (NLP), linguistic research, and language model training, particularly for digitally low-resource languages such as Persian. However, web-derived text often contains document-level noise, even after quality-filtering pipelines. We present HPLT3 Persian Refined (HPLT3-PerRef), a refined Persian web corpus containing 105.9 billion tokens and 102.37 million documents, obtained by applying a supervised quality-filtering pipeline to the Persian subset of HPLT3. We manually annotated 6,144 Persian web documents and trained a multilingual transformer-based classifier to distinguish high- and low-quality documents. The classifier removed approximately 20% of the original corpus while preserving broad domain coverage and complements HPLT3’s heuristic-based Web Document Score (WDS) by identifying additional low-quality documents. We publicly release the corpus, the trained classifier, and the complete source code.
%U https://aclanthology.org/2026.wac-1.6/
%P 60-69
Markdown (Informal)
[HPLT3-PerRef: A Refined Large-Scale Persian Web Corpus via Supervised Document Quality Filtering](https://aclanthology.org/2026.wac-1.6/) (Razzaghi et al., WAC 2026)
ACL