Data-Driven Syllabification for Middle Dutch

Wouter Haverals, F.B. Karsdorp, Mike Kestemont

Research output: Contribution to journal/periodicalArticleScientificpeer-review

Abstract

The task of automatically separating Middle Dutch words into syllables is a challenging one. A first method was presented by Bouma and Hermans (2012), who combined a rule-based finite-state component with data-driven error correction. Achieving an average word accuracy of 96.5%, their system surely is a satisfactory one, although it leaves room for improvement. Generally speaking, rule-based methods are less attractive for dealing with a medieval language like Middle Dutch, where not only each dialect has its own spelling preferences, but where there is also much idiosyncratic variation among scribes. This paper presents a different method for the task of automatically syllabifying Middle Dutch words, which does not rely on a set of pre-defined linguistic information. Using a Recurrent Neural Network (RNN) with Long-Short-Term Memory cells (LSTM), we obtain a system which outperforms the rule-based method both in robustness and in effort.
Original languageEnglish
Pages (from-to)1-23
JournalDigital Medievalist
Volume1
Issue number2
DOIs
Publication statusPublished - 04 Nov 2019

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