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Title
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Data-Driven Syllabification for Middle Dutch
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Author
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Abstract
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| 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. |
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Language
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English
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Source (journal)
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Digital medievalist / University of Lethbridge. - Lethbridge, Alta, 2005, currens
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Related dataset(s)
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Publication
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Lethbridge, Alta
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University of Lethbridge
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2019
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ISSN
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1715-0736
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DOI
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10.16995/DM.83
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Volume/pages
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12
:1,2
(2019)
, p. 1-23
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Full text (Publisher's DOI)
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Full text (open access)
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