Constructing a Recipe Web from Historical Newspapers

Conference paper
Part of the Lecture Notes in Computer Science book series (LNCS, volume 11136)


Historical newspapers provide a lens on customs and habits of the past. For example, recipes published in newspapers highlight what and how we ate and thought about food. The challenge here is that newspaper data is often unstructured and highly varied. Digitised historical newspapers add an additional challenge, namely that of fluctuations in OCR quality. Therefore, it is difficult to locate and extract recipes from them. We present our approach based on distant supervision and automatically extracted lexicons to identify recipes in digitised historical newspapers, to generate recipe tags, and to extract ingredient information. We provide OCR quality indicators and their impact on the extraction process. We enrich the recipes with links to information on the ingredients. Our research shows how natural language processing, machine learning, and semantic web can be combined to construct a rich dataset from heterogeneous newspapers for the historical analysis of food culture.


Natural language processing Information extraction Food history Digitised newspapers Digital humanities 



The authors thank the National Library of the Netherlands for making available the newspaper collection for research purposes as well as for organising the HackaLOD hackathon with Rijksmuseum and Netwerk Digitaal Erfgoed where this project got started. We thank Jesse de Does for the OCR quality measure, Marten Postma and Emiel van Miltenburg for querying Open Dutch WordNet, and Richard Zijdeman for fruitful discussions on the dataset concept. No Hawaiian pizzas were consumed during the writing of this paper.


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© Springer Nature Switzerland AG 2018

Authors and Affiliations

  1. 1.KNAW Humanities Cluster, DHLabAmsterdamThe Netherlands
  2. 2.Universiteit van AmsterdamAmsterdamThe Netherlands

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