Instant Diacritics Restoration System for Sindhi Accent Prediction using N-Gram and Memory-Based Learning Approaches

The script of Sindhi Language is highly complex due to many complexities including abundance of homographic words. The interpretation of the text turns so tough due to the possibility of multitudinal meanings associated with a homographic word unless given specific pronunciation with the help of dia...

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Published in:International journal of advanced computer science & applications 2017-01, Vol.8 (4)
Main Authors: Shaikh, Hidayatullah, Ahmed, Javed, Hussain, Mumtaz
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Language:English
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Language
Learning
Natural language
Natural language processing
Orthography
Restoration
Speech processing
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description The script of Sindhi Language is highly complex due to many complexities including abundance of homographic words. The interpretation of the text turns so tough due to the possibility of multitudinal meanings associated with a homographic word unless given specific pronunciation with the help of diacritics. Diacritics help the readers to comprehend the text easily. Due to the rapidly developing nature of this era, people don’t bother writing diacritics in routine applications of life. Besides creating difficulties for human reading, the absence of diacritics does also make the text abstruse for machine reading. Relatively alike human, machines may also lead to semantic and syntactic complexities during computational processing of the language. Instant diacritics restoration is an approach emerged from the text prediction systems. This type of diacritics restoration is an unprecedented work in the realm of natural language processing, particularly in Indo-Aryan languages. A proposition for a framework using N-Grams and Memory-Based Learning approach is made in this work. The grab-point of this mechanism is its 99.03% accuracy on the corpus of Sindhi language during the experiments. The comparative edge of instant diacritics restoration is its being source of expedition in the performance of other natural language and speech processing applications. The future development of this approach seems vivid and clear for Sindhi orthography is highly similar to those of Arabic, Urdu, Persian and other languages based on this type of script.
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subjects Language
Learning
Natural language
Natural language processing
Orthography
Restoration
Speech processing
title Instant Diacritics Restoration System for Sindhi Accent Prediction using N-Gram and Memory-Based Learning Approaches
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