Data-Driven Syllabification for Middle Dutch. Issue 1 (4th November 2019)

• Record Type: Journal Article
• Authors: Haverals, Wouter; Karsdorp, Folgert; Kestemont, Mike
• Is Part Of: Digital medievalist. Volume 12:Issue 1(2019)
• Publication Date: 2019-11-04
• Subjects: automatic syllabification -- data-driven methods -- recurrent neural network -- Middle Dutch -- orthographic variation; Digital media -- Periodicals; Civilization, Medieval -- Study and teaching -- Periodicals; Middle Ages -- Study and teaching -- Periodicals; 909.07
• DOI: 10.16995/dm.83
• Languages: English
• ISSNs: 1715-0736

Abstract

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.