Logo Lanfrica
  • Home
  • Atlas
  • Insights
  • Docs
  • Sign in

© 2026 Lanfrica. All rights reserved. All copyrights of the resources shown on the Lanfrica website belong to the original copyright holders, unless explicitly stated otherwise.

Neural networks as possible hyphenation technique for Afrikaans

Domain:

natural language processing

Record type:

paper
Creator:
Mac
Publisher:
Med
Host:
In Afrikaans compound words are written as one word. New words are therefore created by simply joining words. Word hyphenation during typesetting by computer is often a problem, because the source of reference changes all the time. A neural network (feedforward backpropagation) was trained with about 5 000 Afrikaans words with correct syllabification. The neural network classified 97,56% of possible points in 5 000 randomly chosen words correctly as either valid or invalid hyphenation points. In a test with 510 words from an Afrikaans magazine the neural network classified 98,75% of possible positions correctly. We came to the conclusion that neural networks can be used successfully as hyphenation technique for Afrikaans.

Visit

doi.org

Tasks

text normalization

Languages

Afrikaans

Licenses

https://creativecommons.org/licenses/by/4.0

Similar

Computer hyphenation of AfrikaansModelling flow dynamics in water distribution networks using artificial neural networks - A leakage detection techniqueNeural-Genetic Algorithm as Feature Selection Technique for Determining Sunagoke Moss Water ContentEfficient Convolutional Neural Networks for Diacritic RestorationNeural Networks and Q-Learning for RoboticsNeural Networks Architecture for Amazigh POS Tagging

Computer hyphenation of Afrikaans

Modelling flow dynamics in water distribution networks using artificial neural networks - A leakage detection technique

Computational approaches can be used to detect leakages in water distribution networks. One such app

Neural-Genetic Algorithm as Feature Selection Technique for Determining Sunagoke Moss Water Content

Efficient Convolutional Neural Networks for Diacritic Restoration

Diacritic restoration has gained importance with the growing need for machines to understand written

Neural Networks and Q-Learning for Robotics

International audience IntroductionBehavior-Based ApproachSupervised Learning of a Be

Neural Networks Architecture for Amazigh POS Tagging