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

arXiv: https://arxiv.org/abs/2205.00716 · HTML: https://mlsanity.com/paper/2205.00716/

## Key facts

- Cited by 1 papers in the map (data through 14 Oct 2026).
- Builds on 10 of its 35 references that are in the map.
- First posted on arXiv 2 May 2022.

## Builds on (most cited first, 10 of 10)

- [BERT: Pre-training of Deep Bidirectional Transformers for Language Understanding](https://mlsanity.com/paper/1810.04805/) (2018)
- [Efficient Estimation of Word Representations in Vector Space](https://mlsanity.com/paper/1301.3781/) (2013)
- [BioBERT: a pre-trained biomedical language representation model for biomedical text mining](https://mlsanity.com/paper/1901.08746/) (2019)
- [Stanza: A Python Natural Language Processing Toolkit for Many Human Languages](https://mlsanity.com/paper/2003.07082/) (2020)
- [Machine Knowledge: Creation and Curation of Comprehensive Knowledge Bases](https://mlsanity.com/paper/2009.11564/) (2020)
- [A Survey on Open Information Extraction](https://mlsanity.com/paper/1806.05599/) (2018)
- [CESI: Canonicalizing Open Knowledge Bases using Embeddings and Side Information](https://mlsanity.com/paper/1902.00172/) (2019)
- [OpenIE6: Iterative Grid Labeling and Coordination Analysis for Open Information Extraction](https://mlsanity.com/paper/2010.03147/) (2020)
- [In Layman's Terms: Semi-Open Relation Extraction from Scientific Texts](https://mlsanity.com/paper/2005.07751/) (2020)
- [Open Information Extraction on Scientific Text: An Evaluation](https://mlsanity.com/paper/1802.05574/) (2018)

## Built on by (most cited first, 1 of 1)

- [A Library Perspective on Supervised Text Processing in Digital Libraries: An Investigation in the Biomedical Domain](https://mlsanity.com/paper/2411.12752/) (2024)

## 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.