digital librarians

Abstract: In library catalogues based on MARC21 bibliographic metadata schema there are about a dozen data elements that catalogers can utilize to record information about the different aspects of translations: whether or not the edition is a translation, source and target language, title of the original work, translator and other contributors, publication data of the original work, literary genre. Libraries however rarely explore the full arsenal, thus to find translation is not a trivial task. The paper describes these important MARC21 data elements, and shows a use case of finding translations in five catalogues (of the Norwegian, Croatian, Polish, Belgian and Dutch national libraries) explaining the differences among the practices, and the results.

Abstract: Digital libraries and knowledge graphs need a way to organize very large collections so that related works are close and any work is quick to reach. The classifications libraries inherited for this, such as LCC and DDC, are maintained by editorial staff, are partly closed, and were not built against a stated, measurable objective. We propose the opposite: a general classification that is fitted, under the constraints that make a classification usable on a shelf or in a browse list, to the same quantity that a companion survey and benchmark (Awad, 2026) ranks existing schemes by. The objective is J = D + 0.005 N, where D is the normalized Dasgupta cost of the class tree on a fixed similarity graph (Dasgupta, 2016) and N is an expected navigation cost in bits with a per-level charge. Two constraints are hard: every class is a contiguous run of one fixed shelf order (the optimal-alphabetic-tree constraint of Hu and Tucker, 1971), and new works are filed by splitting leaf classes only, so that every existing class keeps its code, label and parent. On a corpus of 10,000 arXiv papers the fitted prototype has 110 hand-labelled classes and reaches cost 0.1722 in sample (average linkage 0.1518; arXiv's own categories 0.3333; the best existing scheme reached through the same lexical classifier 0.6414, from the survey). Filing 500 library books as new works created 16 classes and changed no existing identifier. Fit, filing and scoring share one embedding: the held-out skill of 0.922 falls to 0.896 when the same fit is scored on the graphs of the two other models, and on books the average falls from 0.806 to 0.731. The scheme has also seen only arXiv, and labels for inserted classes are weak. We state those limits plainly, release the scheme as CC0 open data, and ask whether a learned, open, order-preserving classification can become a general one.

Abstract: Tibetan, spoken by some seven million people on and around the Qinghai-Xizang Plateau, remains underrepresented in natural language processing (NLP). We present, to our knowledge, the first peer-reviewed systematic bibliometric survey of Tibetan NLP: 140 publications from 2004 to 2026 in the ACL Anthology and DBLP, annotated for venue type, research area, first-affiliation country, and resource release. Output has grown rapidly, with 84% of the publications appearing since 2018, and has expanded bottom-up along the NLP pipeline, from corpora and word segmentation through script, speech, and applications to large language models (LLMs). Researchers based in China first-authored 87% of the publications, which span all 25 represented research areas, whereas work from other countries concentrates on Classical Tibetan. We argue that this full-pipeline paradigm, realized under the compounded constraints of a complex script, dialectal diversity, and geographic remoteness, offers a reference model for other low-resource languages of the Global South. We also identify gaps in dialect coverage, responsible NLP, native and comparable evaluation, cross-community visibility, and resource release (only 29% of the publications release data, code, or models), and recommend open science, shared standards, and community participation for the LLM era.

Abstract: URLs in scholarly documents link to rich external resources such as datasets, software, publications, and websites. Extracting these URLs is crucial in the data preparation stage of many downstream tasks, such as link rot analysis, web crawling, and building knowledge graphs. However, existing studies often downplay this phase, simply extracting URLs from a single format, usually text directly converted from PDFs. We present a systematic study evaluating URL extraction across six input formats (text with annotation layer, LaTeX, HTML, XML, Markdown, and PNG converted from PDF). To support the evaluation, we compiled a benchmark dataset consisting of 2,338 manually annotated URLs from 200 arXiv papers spanning a wide range of domains over a 33-year period. In addition to evaluating individual file formats, we also compared 63 composite input-format combinations. Our extensive evaluations indicate that TEXTWAL achieves the best performance among single-format inputs, while TEXTWAL+LaTeX achieves the best overall URL extraction performance. The same trend is observed for URLs linking to open-access datasets and software. To further validate these findings, we apply our format-specific URL extraction pipelines to a longitudinal random sample of 364,744 arXiv papers spanning 33 years. We observe a sharp increase in URL density after 2015, along with remarkable differences in URL extraction across file formats over time. Overall, our study highlights the importance of selecting an appropriate format for URL extraction from scholarly documents. The dataset and code are publicly available at: https://github.com/lamps-lab/arxiv-url-bench .