An Extreme Multi-label Text Classification (XMTC) Library Dataset: What if we took "Use of Practical AI in Digital Libraries" seriously?
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arXiv
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| Autores principales: | , , , , , , , |
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| Formato: | Preprint |
| Publicado: |
2026
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| _version_ | 1866911506367512576 |
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| author | D'Souza, Jennifer Sadruddin, Sameer Kähler, Maximilian Salfinger, Andrea Zaccagna, Luca Incitti, Francesca Snidaro, Lauro Suominen, Osma |
| author_facet | D'Souza, Jennifer Sadruddin, Sameer Kähler, Maximilian Salfinger, Andrea Zaccagna, Luca Incitti, Francesca Snidaro, Lauro Suominen, Osma |
| contents | Subject indexing is vital for discovery but hard to sustain at scale and across languages. We release a large bilingual (English/German) corpus of catalog records annotated with the Integrated Authority File (GND), plus a machine-actionable GND taxonomy. The resource enables ontology-aware multi-label classification, mapping text to authority terms, and agent-assisted cataloging with reproducible, authority-grounded evaluation. We provide a brief statistical profile and qualitative error analyses of three systems. We invite the community to assess not only accuracy but usefulness and transparency, toward authority-anchored AI co-pilots that amplify catalogers' work. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2603_10876 |
| institution | arXiv |
| publishDate | 2026 |
| record_format | arxiv |
| spellingShingle | An Extreme Multi-label Text Classification (XMTC) Library Dataset: What if we took "Use of Practical AI in Digital Libraries" seriously? D'Souza, Jennifer Sadruddin, Sameer Kähler, Maximilian Salfinger, Andrea Zaccagna, Luca Incitti, Francesca Snidaro, Lauro Suominen, Osma Computation and Language Artificial Intelligence Digital Libraries Information Retrieval Subject indexing is vital for discovery but hard to sustain at scale and across languages. We release a large bilingual (English/German) corpus of catalog records annotated with the Integrated Authority File (GND), plus a machine-actionable GND taxonomy. The resource enables ontology-aware multi-label classification, mapping text to authority terms, and agent-assisted cataloging with reproducible, authority-grounded evaluation. We provide a brief statistical profile and qualitative error analyses of three systems. We invite the community to assess not only accuracy but usefulness and transparency, toward authority-anchored AI co-pilots that amplify catalogers' work. |
| title | An Extreme Multi-label Text Classification (XMTC) Library Dataset: What if we took "Use of Practical AI in Digital Libraries" seriously? |
| topic | Computation and Language Artificial Intelligence Digital Libraries Information Retrieval |
| url | https://arxiv.org/abs/2603.10876 |