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The paper presents a fully self‑supervised contrastive learning framework that learns lexical representations from raw IPA‑transcribed wordlists without any cognacy annotations or expert input. Using a dual contrastive objective—word‑level and language‑level losses—the model produces word representations that enable fast computation of pairwise language distances and the inference of a global phylogenetic tree for 3,399 language varieties. The resulting tree achieves a generalized quartet distance competitive with baselines while requiring only minutes of computation on a standard notebook GPU, and the representations also capture diachronic concept stability.
arXiv:2606. 24984v1 Announce Type: new Abstract: Learning representations that remain robust across centuries of variation in handwriting is a key challenge in diachronic representation learning.
HERBIOME is a modular, end‑to‑end pipeline that automates the digitization of herbarium labels. It combines YOLOv8 for component detection, CRAFT Hezar for word‑level text localization, a fine‑tuned TrOCR model for mixed handwritten and printed text recognition, and GPT‑4o Mini for structuring metadata into standardized fields. Evaluation on 450 French specimens shows high surface similarity (MWS ≈ 0.616) and moderate semantic accuracy (SMA ≈ 0.442), with taxonomic fields identified as the main challenge.
arXiv:2607. 04147v1 Announce Type: cross Abstract: Automated fine-grained perception of calligraphy styles--a task vital to cultural heritage preservation--remains a critical challenge for Large Vision-Language Models (LVLMs), largely constrained by existing datasets that suffer from modal mixture and flattened labels.
arXiv:2608. 11741v1 Announce Type: cross Abstract: The scholarly exegesis of ancient Chinese characters demands integrating visual observation, linguistic analysis, and historical context.
The scholarly exegesis of ancient Chinese characters demands integrating visual observation, linguistic analysis, and historical context. However, existing computational approaches focus narrowly on subtasks such as character recognition and retrieval, lacking the structured datasets and benchmarks required for comprehensive scholarly analysis.