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Translating text into pictographs

Published online by Cambridge University Press:  11 November 2015

VINCENT VANDEGHINSTE ,
INEKE SCHUURMAN LEEN SEVENS  and
FRANK VAN EYNDE
VINCENT VANDEGHINSTE
Affiliation:
Centre for Computational Linguistics, University of Leuven, Blijde Inkomststraat 21 - bus 3315 B-3000, Leuven, Belgium e-mails: [email protected], [email protected], [email protected]
INEKE SCHUURMAN LEEN SEVENS
Affiliation:
Centre for Computational Linguistics, University of Leuven, Blijde Inkomststraat 21 - bus 3315 B-3000, Leuven, Belgium e-mails: [email protected], [email protected], [email protected]
FRANK VAN EYNDE
Affiliation:
Centre for Computational Linguistics, University of Leuven, Blijde Inkomststraat 21 - bus 3315 B-3000, Leuven, Belgium e-mails: [email protected], [email protected], [email protected]
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Abstract

We describe and evaluate a text-to-pictograph translation system that is used in an online platform for Augmentative and Alternative Communication, which is intended for people who are not able to read and write, but who still want to communicate with the outside world. The system is set up to translate from Dutch into Sclera and Beta, two publicly available pictograph sets consisting of several thousands of pictographs each. We have linked large amounts of these pictographs to synsets or combinations of synsets of Cornetto, a lexical-semantic database for Dutch similar to WordNet. In the translation system, the Dutch input text undergoes shallow linguistic analysis and the synsets of the content words are looked up. The system looks for the nearest pictographs in the lexical-semantic database and displays the message into pictographs. We evaluated the system and results showed a large improvement over the baseline system which consisted of straightforward string-matching between the input text and the filenames of the pictographs.

Our system provides a clear improvement in the communication possibilities of illiterate people. Nevertheless there is room for further improvement.

Type
Articles
Information
Natural Language Engineering , Volume 23 , Issue 2 , March 2017 , pp. 217 - 244
Copyright
Copyright © Cambridge University Press 2015 

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