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MaSMT: A Multi-agent system development framework for English-Sinhala machine translation

Show simple item record Hettige, B Karunananda, AS Rzevski, G 2018-05-16T21:44:10Z 2018-05-16T21:44:10Z
dc.identifier.issn 2279 - 0756 en_US
dc.description.abstract Multi-agent System gives high quality solutions through communication, negotiation and coordination among agents. Agents are small self-contained computational objects capable of exchanging messages among themselves. Number of general purpose toolkits and frameworks are available to develop Multi-agent systems for modelling complex real world problems. However, none of them has been specialized for the area of natural language processing specially machine translation. MaSMT is a java based multi-agent system development framework, especially designed for development of English to Sinhala machine translation system. MaSMT provides two types of agents, namely ordinary agents and manager agents. A manager agent consists of number of ordinary agents within its control. Further, manager agents can directly communicate with other manager agents and each and every ordinary agent in the swarm is assigned to a particular manager agent. An ordinary agent in a swarm can directly communicate only with the agents in its own swarm and its manager agent. The framework primarily implements object-object communication, XML-based data passing and MySQL database connectivity to use agents’ ontology and message passing. Agent communication in the framework has been implemented to comply with FIFA-ACL specification. MaSMT framework is used to develop English to Sinhala machine translation system and Word Reader which is capable of analysing a given English word. Experimental result shows that, MaSMT framework can be used to develop Natural Language Processing applications successfully. en_US
dc.language.iso en en_US
dc.subject Machine Translation, Multi-agent Systems. en_US
dc.title MaSMT: A Multi-agent system development framework for English-Sinhala machine translation en_US
dc.type Article-Abstract en_US
dc.identifier.year 2013 en_US
dc.identifier.journal International Journal of Computational Linguistics and Natural Processing en_US
dc.identifier.issue 7 en_US
dc.identifier.volume 2 en_US
dc.identifier.pgnos pp. 411 - 416 en_US en_US en_US en_US

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