An OLAC Extension for Dravidian Languages
B Prabhulla Chandran Pillai
TL;DR
This paper proposes an extension to the OLAC framework to better support Dravidian languages by incorporating an ontological structure, enhancing natural language processing capabilities over traditional statistical methods.
Contribution
It introduces a new architectural extension for OLAC tailored to Dravidian languages, emphasizing ontological structures for improved NLP.
Findings
Ontological approach offers advantages over statistical methods in NLP.
Extension improves resource organization for Dravidian languages.
Framework supports more effective language processing.
Abstract
OLAC was founded in 2000 for creating online databases of language resources. This paper intends to review the bottom-up distributed character of the project and proposes an extension of the architecture for Dravidian languages. An ontological structure is considered for effective natural language processing (NLP) and its advantages over statistical methods are reviewed
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Taxonomy
TopicsNatural Language Processing Techniques · Algorithms and Data Compression · Speech and dialogue systems