Data-Driven Syllabification for Middle Dutch

Wouter Haverals, F.B. Karsdorp, Mike Kestemont

Onderzoeksoutput: Bijdrage aan wetenschappelijk tijdschrift/periodieke uitgaveArtikelWetenschappelijkpeer review


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.
Originele taal-2Engels
Pagina's (van-tot)1-23
TijdschriftDigital Medievalist
Nummer van het tijdschrift2
StatusGepubliceerd - 04 nov. 2019


Duik in de onderzoeksthema's van 'Data-Driven Syllabification for Middle Dutch'. Samen vormen ze een unieke vingerafdruk.

Citeer dit