DocumentCode :
2878036
Title :
A rule-based approach for building an artificial English-ASL corpus
Author :
Tmar, Zouhour ; Othman, Ali ; Jemni, Mohamed
Author_Institution :
Res. Lab. LaTICE, Univ. of Tunis, Tunis, Tunisia
fYear :
2013
fDate :
21-23 March 2013
Firstpage :
1
Lastpage :
4
Abstract :
A serious problem facing the Community for researchers in the field of sign language is the absence of a large parallel corpus for signs language. The ASLG-PC12 project, proposes a rule-based approach for building big parallel corpus between English written texts and American Sign Language Gloss. We present a novel algorithm which transforms an English part-of-speech sentence to ASL gloss. This project was started in the beginning of 2010, a part of the project WebSign, and it offers today a corpus containing more than one hundred million pairs of sentences between English and ASL gloss. It is available online for free in order to develop and design new algorithms and theories for American Sign Language processing, for example statistical machine translation and any related fields. In this paper, we present tasks for generating ASL sentences from the corpus Gutenberg Project that contains only English written, texts.
Keywords :
knowledge based systems; natural language processing; sign language recognition; text analysis; ASL gloss; ASL sentences; ASLG-PC12 project; American sign language gloss; American sign language processing; English part-of-speech sentence; English written text; Gutenberg project; WebSign; artificial English-ASL corpus; parallel corpus; rule-based approach; statistical machine translation; Assistive technology; Buildings; Communities; Educational institutions; Gesture recognition; Pragmatics; Transforms; Natural Language Processing; Parallel Corpora; Sign Language;
fLanguage :
English
Publisher :
ieee
Conference_Titel :
Electrical Engineering and Software Applications (ICEESA), 2013 International Conference on
Conference_Location :
Hammamet
Print_ISBN :
978-1-4673-6302-0
Type :
conf
DOI :
10.1109/ICEESA.2013.6578458
Filename :
6578458
Link To Document :
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