Thesis (MSc (Computer Science))--University of Stellenbosch, 2005. / Sign Languages such as South African Sign Language, are proper natural languages;
they have their own vocabularies, and they make use of their own grammar
rules.
However, machine translation from a spoken to a signed language creates interesting
challenges. These problems are caused as a result of the differences in
character between spoken and signed languages. Sign Languages are classified as
visual-spatial languages: a signer makes use of the space around him, and gives
visual clues from body language, facial expressions and sign movements to help
him communicate. It is the absence of these elements in the written form of a
spoken language that causes the contextual ambiguities during machine translation.
The work described in this thesis is aimed at resolving the ambiguities caused
by a translation from written English to South African Sign Language. We
designed and implemented a preprocessor that uses areas of linguistics such as
anaphora resolution and a data structure called a scene graph to help with the
spatial aspect of the translation. The preprocessor also makes use of semantic
and syntactic analysis, together with the help of a semantic relational database,
to find emotional context from text. This analysis is then used to suggest body
language, facial expressions and sign movement attributes, helping us to address
the visual aspect of the translation.
The results show that the system is flexible enough to be used with different
types of text, and will overall improve the quality of a machine translation from
English into a Sign Language.
| Identifer | oai:union.ndltd.org:netd.ac.za/oai:union.ndltd.org:sun/oai:scholar.sun.ac.za:10019.1/2832 |
| Date | 12 1900 |
| Creators | Combrink, Andries J. |
| Contributors | Van Zijl, L., University of Stellenbosch. Faculty of Science. Dept. of Mathematical Sciences. Institute for Applied Computer Science. |
| Publisher | Stellenbosch : University of Stellenbosch |
| Source Sets | South African National ETD Portal |
| Detected Language | English |
| Type | Thesis |
| Format | 689961 bytes, application/pdf |
| Rights | University of Stellenbosch |
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