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.

Web registers, the situationally defined varieties of online text, are almost always annotated at document level. A document, however, may contain multiple such varieties. We label 80,000 documents from the HPLT web corpus at segment level in seven languages using the open-weight Gemma 4, reaching Cohen’s κ=0.73 against 1,878 human-annotated segments, comparable to previously reported document-level agreement. We find that most documents have two or more registers, and that they recur in similar positions and combinations across the studied languages. Yet on average, a single register accounts for 73% of the labeled text, and a quarter to a third of segments receive no label. Document-level labels are therefore mostly correct but incomplete. Multi-dimensional analysis shows that the segment-level labels correspond to linguistic differences. In all seven languages, registers are ordered the same way, from informational to involved text.

Despite recent advances in automatic web register (genre) labeling and its applications to web-scale datasets and LLM development, the effectiveness of these tools for digitally lowresource languages remains unclear. This study introduces ParsCORE, the first largescale collection of Persian web registers (genres), and evaluates deep learning models for register classification and keyword analysis across major registers. Using 2,000 humanannotated documents, the models achieved a micro F1-score of 0.76. The findings provide a foundation for future research on the linguistic and cultural specificities of Persian registers.

Nearly all studies on web registers—online text varieties associated with characteristic social contexts and linguistic features—use full documents as the unit of analysis. However, web documents often contain sections in different registers. A cooking blog, for instance, may combine personal storytelling, recipe instructions, user comments, and promotional text within a single URL. This internal variation raises doubts about the validity of document level register labeling. In this paper, we propose an LLM-based approach that identifies register homogeneous segments within documents and apply it to a 10,000-document English sample from HPLT 3.0. We show that segmentation addresses persistent problems in register analysis, including low inter-annotator agreement and category fuzziness. Strikingly, it also reveals that most web documents contain more than one register, making register mixing the norm rather than the exception on the web.