Original Research

Afrikaans Syllabification Patterns

Tilla Fick, Chris J. Swanepoel
Suid-Afrikaanse Tydskrif vir Natuurwetenskap en Tegnologie | Vol 29, No 2 | a9 | DOI: https://doi.org/10.4102/satnt.v29i2.9 | © 2010 Tilla Fick, Chris J. Swanepoel | This work is licensed under CC Attribution 4.0
Submitted: 13 January 2010 | Published: 13 January 2010

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Tilla Fick, Universiteit van Suid-Afrika, South Africa
Chris J. Swanepoel, Universiteit van Suid-Afrika, South Africa

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Abstract

In contrast to English, automatic hyphenation by computer of Afrikaans words is a problem that still needs to be addressed, since errors are still often encountered in printed text. An initial step in this task is the ability to automatically syllabify words. Since new words are created continuously by joining words, it is necessary to develop an “intelligent” technique for syllabification. As a first phase of the research, we consider only the orthographic information of words, and disregard both syntactic and morphological information. This approach allows us to use machine-learning techniques such as artificial neural networks and decision trees that are known for their pattern recognition abilities. Both these techniques are trained with isolated patterns consisting of input patterns and corresponding outputs (or targets) that indicate whether the input pattern should be split at a certain position, or not. In the process of compiling a list of syllabified words from which to generate training data for the  syllabification problem, irregular patterns were identified. The same letter patterns are split differently in different words and complete words that are spelled identically are split differently due to meaning. We also identified irregularities in and between  the different dictionaries that we used. We examined the influence range of letters that are involved in irregularities. For example, for their in agter-ente and vaste-rente we have to consider three letters to the left of r to be certain where the hyphen should be inserted. The influence range of the k in verstek-waarde and kleinste-kwadrate is four to the left and three to the right. In an analysis of letter patterns in Afrikaans words we found that the letter e has the highest frequency overall (16,2% of all letters in the word list). The frequency of words starting with s is the highest, while the frequency of words ending with e is the highest. It is important to note that the frequency of words ending with s is even higher than for words starting with s. The two and three letter patterns that occur most are er (10% of all two letter patterns) and ing (4% of all three letter patterns). In an analysis of syllables in Afrikaans words, we found that (as for complete words) syllables most often start with the letter s and end with e, while the frequency of syllables ending with s is almost as high as the frequency of syllables starting with s. This indicates that problems with hyphenation can be expected around the letter s. The two and three letter syllables that occur most often are -ge- and -ver-, respectively.

In an attempt to decide on the window length to use to generate training data for machine-learning techniques we also analysed the length of syllables. The results show that two and three letter syllables occur most often, but that four letter syllables have the most unique instances. We also analysed a spectrum of window configurations and found that the ideal configuration will have to be determined empirically.

One major problem we identified in this study is that irregular syllabification often occurs where letter patterns include the letter s. The reasons being (i) the use of the combining s when joining words, (ii) almost equal frequencies of syllables starting and ending with s and (iii) vague hyphe- nation rules for letter combinations containing s. To effectively address automatic syllabification in Afrikaans, it is necessary to develop more sophisticated methods to handle vagueness around the letter s.

 


Keywords

Lettergreepverdeling; onreëlmatigheid; masjienleertegnieke; kunsmatige neurale netwerke; beslissingsbome; patroonherkenning; afrigting.

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