pylazaro is an open‑source Python package that automatically extracts unassimilated lexical borrowings—primarily anglicisms—from Spanish text. It provides a unified interface to five sequence‑labeling models trained with different libraries, allowing users to switch models without handling library idiosyncrasies. The package outperforms general‑purpose large language models (F1 <0.40) with its best model achieving an F1 of 0.86, and has been downloaded over 58,000 times, powering the Observatorio Lazaro resource that tracks anglicism usage in Spanish media.
By Elena Alvarez-Mellado
Wieszcz‑XIX is a 6.75‑billion‑token corpus of Polish texts from 1800‑1918, comprising 3.1 billion words across 294,369 documents, assembled from Wolne Lektury and the Internet Archive. The pipeline filters, deduplicates, audits for post‑1918 leakage, and splits at the document level, yielding a training set with only 0.04–0.38 % post‑1918 residue. From this corpus the authors train decoder‑only language models ranging from 47 M to 349 M parameters, demonstrating that larger models better preserve period spelling and exhibit temporal boundedness compared to modern Polish baselines.
By Szymon Kocur
arXiv:2603.10861v2 Announce Type: replace
Abstract: SiDiaC-v.2.0 is the largest comprehensive Sinhala Diachronic Corpus to date, covering a period from 1800 CE to 1955 CE in terms of publication date...
By Nevidu Jayatilleke, Nisansa de Silva, Uthpala Nimanthi, Gagani Kulathilaka, Azra Safrullah, Johan Sofalas
arXiv:2611.02940v1 Announce Type: new
Abstract: PlagiaCorpus is a bilingual Arabic-English dataset for benchmarking plagiarism and text-reuse detection, including reuse produced by large language mod...
By Adel Ammar, Serry Sibaee, Wadii Boulila
arXiv:2608.18097v2 Announce Type: replace
Abstract: We present FrenchNews-7, a cross-publisher France-based French-language news editorial desk classification benchmark combining a large multi-outlet...
By Amr Sobhy
The paper surveys language models created for Portuguese, noting that while rapid progress has been made in NLP, development has been uneven across languages. It systematically maps 46 Portuguese models, detailing aspects such as base model, architecture, resources, datasets, licensing, code, data, and weights. The study also traces model evolution phylogenetically, highlights research gaps, and outlines future directions for Portuguese language modeling.
By Jhessica Silva, Carlos Caetano, Helena Maia, Breno Bernard Nicolau de Fran\c{c}a, Sandra Avila, Helio Pedrini
The paper introduces Loci Similes, a benchmark for detecting intertextual links in Latin literature. It provides a curated dataset of about 176,000 text segments and 1,490 expert-verified parallels, including 945 labeled references from an existing source. Baselines for retrieval and classification are established using both lexical methods and pretrained encoder language models.
By Julian Schelb, Michael Wittweiler, Marie Revellio, Barbara Feichtinger, Andreas Spitz
The paper shows that HuggingFace’s ByteLevel pre‑tokenizer, which treats a word as a sequence of Unicode letters, splits abugida scripts at every vowel sign, creating a training‑free lower bound on tokenizer fertility. Across 26 languages, all 17 abugidas exhibit increased token counts (up to 9×), while Latin, Cyrillic, Hangul, and Han remain unchanged. The authors demonstrate that correcting the character class reduces Nepali token counts, improves model performance, and that this issue is widespread in popular HuggingFace models.
By Sajal Regmi, Siddhartha Pudasaini, Chetan Phakami Pun
arXiv:2606.27314v2 Announce Type: replace
Abstract: To avoid moderation and surveillance on social media, some users routinely invent indirect linguistic expressions (ILE) that camouflage sensitive m...
By Hamid Reza Firoozfar, Mohammadsadegh Abolhasani, Reza Mousavi, Paul Jen-Hwa Hu
arXiv:2604.08568v3 Announce Type: replace-cross
Abstract: The widespread use of LLM-based writing assistance has raised an interesting question about the homogenization of English. As LLMs tend to re...
By Nabelanita Utami, Ryohei Sasano
The paper presents a quantitative method for analyzing historical change in Italian media by reconstructing a diachronic corpus of about 600,000 articles from "La Repubblica" (1985‑2000). Using NLP to examine lexical and semantic patterns, and tools from complex systems, the authors identify shifts in media discourse, such as Italy’s transition from the First to the Second Republic and major international conflicts like the Gulf and Kosovo Wars. They find that war periods correspond to lower semantic entropy, indicating a more focused discourse.
By Dario Zarcone, Salvatore Miccich\`e, David Sanchez
arXiv:2607. 09316v1 Announce Type: cross Abstract: Thematic indexing -- the practice of assigning structured conceptual labels to sections of text -- is essential to scholarly access in large-scale literary and historical editions, yet it remains a largely manual, labour-intensive process.
By Miguel Arana-Catania, Gillian Pink, Glenn Roe