mlsanityData to 7 Oct 2026

A Library Perspective on Nearly-Unsupervised Information Extraction Workflows in Digital Libraries

arXiv 2205.00716 ↗First posted 2 May 2022cs.CL cs.DLcited by 1

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Abstract (arXiv)

Information extraction can support novel and effective access paths for digital libraries. Nevertheless, designing reliable extraction workflows can be cost-intensive in practice. On the one hand, suitable extraction methods rely on domain-specific training data. On the other hand, unsupervised and open extraction methods usually produce not-canonicalized extraction results. This paper tackles the question how digital libraries can handle such extractions and if their quality is sufficient in practice. We focus on unsupervised extraction workflows by analyzing them in case studies in the domains of encyclopedias (Wikipedia), pharmacy and political sciences. We report on opportunities and limitations. Finally we discuss best practices for unsupervised extraction workflows.

Key facts. Cited by 1 paper in the map (data through 7 Oct 2026). Builds on 10 of its 35 references that are in the map. First posted on arXiv 2 May 2022.

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10 of 35 references.

References · 10

  1. 2018BERT: Pre-training of Deep Bidirectional Transformers for Language Understandingcited by 40,204
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  8. 2020OpenIE6: Iterative Grid Labeling and Coordination Analysis for Open Information Extractioncited by 11
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Citing papers per year: 2024: 1.

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  1. 2024A Library Perspective on Supervised Text Processing in Digital Libraries: An Investigation in the Biomedical Domaincited by 0