Source-target mapping model of streaming data flow for machine translation

Streaming information flow allows identification of linguistic similarities between language pairs in real time as it relies on pattern recognition of grammar rules, semantics and pronunciation especially when analyzing so called international terms, syntax of the language family as well as tenses transitivity between the languages. Overall, it provides a backbone translation knowledge for building automatic translation system that facilitates processing any of various abstract entities which combine to specify underlying phonological, morphological, semantic and syntactic properties of linguistic forms and that act as the targets of linguistic rules and operations in a source language following professional human translator. Streaming data flow is a process of mining source data into target language transformation during which any inference impedes the system effectiveness by producing incorrect translation. We address a research problem of exploring streaming data from source-target parallels for detection of linguistic similarities between languages originated from different groups.