Assessing Dutch Syllabification Algorithms and Improving Accuracy by Combining Phonetic and Orthographic Information through Deep Learning
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This paper shows that This research evaluates four Dutch syllabification algorithms, including a new deep-learning model, achieving 99.65% accuracy, a 0.14% improvement over existing methods.
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This research evaluates four Dutch syllabification algorithms, including a new deep-learning model, achieving 99.65% accuracy, a 0.14% improvement over existing methods. Combining phonetic and orthographic data enhances performance, particularly in resolving orthographic ambiguities. Future applications may extend to other languages.
Key Points
- Four algorithms were tested: Brandt Corstius, Liang, Trogkanis-Elkan (CRF), and a new deep-learning model.
- Data-driven algorithms outperformed knowledge-based algorithms in all but one dataset condition.
- The new model achieved a word accuracy of 99.65%, improving by 0.14% over previous best results.
- Phonetic information significantly improved syllabification for words with orthographic ambiguity.
- The developed deep learning frameworks could be adapted for syllabification in other languages.
Paper Resources
Article Content
From source RSS / original summaryarXiv:2605. 28834v1 Announce Type: new Abstract: Syllabification describes the task of dividing words into syllables. Due to many rules and exceptions, training an algorithm to perform syllabification with high accuracy remains a challenge. Throughout the last decades, different algorithms have been put forth for Dutch syllabification, yet a comprehensive comparative assessment has not been done.
Additionally, deep learning has gained significant popularity within NLP in recent years, yet no modern deep-learning based framework has been developed for Dutch orthographic syllabification. Finally, phonetic and orthographic syllabification algorithms have been examined separately, but not in combination.
The aim of the current research was twofold: (a) to examine the performance of existing Dutch syllabification algorithms, and (b) to investigate whether combining phonetic and orthographic information into a single model can increase syllabification performance. To compare the performance of algorithms, four algorithms (Brandt Corstius, Liang, Trogkanis-Elkan (CRF), and a newly conceived deep-learning model) were applied to three different datasets (dictionary words, loanwords, pseudowords).
The algorithms show varying performance across datasets, with the data-driven algorithms outperforming a knowledge-based algorithm in all but one condition. The new deep-learning methods developed led to increased performance compared to the best found in the literature (99. 65% word accuracy, a 0. 14% improvement).
An analysis of the words for which adding phonetic information improved syllabification performance indicates that these were words in which the orthographic ambiguity could be resolved by information on pronunciation. Future research could examine other areas where phonetic information can benefit orthographic processing. In addition, the newly developed deep learning frameworks can be applied to other languages than Dutch.
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