Linguistic Enrichment of Historical Dutch using Deep Learning

Silke Creten, Peter Dekker, Vincent Vandeghinste

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This article discusses the automatic linguistic enrichment of historical Dutch corpora through the use of part-of-speech tagging and lemmatization. Such a type of enrichment facilitates linguistic research where manual annotation is unfeasible. We built a neural network-based model using the PIE framework and performed an in-depth error analysis, in order to identify the strengths and weaknesses of each approach with respect to labeling historical data. In order to do so, we experimented with two data sets: the Corpus Gysseling (13th century texts) and the Corpus van Reenen/Mulder (14th century texts). We used two different statistical approaches (MBT and HunPos) as baselines for our neural approach. MBT is a memory-based tagger frequently used for modern Dutch, while HunPos is an open source trigram tagger. We present thoroughly analyzed results. In general, the neural model scores better than the two baselines, even with limited training data. Based on the error analysis, we propose several strategies for future research in order to improve the labeling of historical Dutch.
Original languageEnglish
Pages (from-to)57-72
Number of pages16
JournalComputational Linguistics in the Netherlands Journal
Publication statusPublished - 13 Dec 2020
EventCOMPUTATIONAL LINGUISTICS IN THE NETHERLANDS - Utrecht University, Utrecht, Netherlands
Duration: 30 Jan 2020 → …
Conference number: 30


  • linguistic enrichment
  • pos tagging
  • neural networks
  • deep learning
  • historical linguistics
  • dutch
  • historical dutch


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