Translation Memory for a Machine Translation System Using the Hadoop Framework

File model approach to optimize the performance of Tree Adjoining Grammar based Machine Translation

“…While grid and cloud computing [1], [17] and [3] have also been applied to resolve the problem. In addition, one experiment has carried out on Machine translation system on a computing cluster that uses the Hadoop framework and carry out in distributed environment by [16]. Here, Translation Memory aids the MT by avoiding processing time on repetitive translation by using balancing algorithm to distribute multiple sentences [2].…”

“…Some other Experiments carried out parallelization on the GARUDA grid by [15] where MT application has been port on Garuda by using MPI framework for achieving speedup and improving performance of System. Fundamental difference of approach is that, these work [7] tries to collect corpora from world wide web, some other work require to port MT application in different environment by [1], [15]and [16] i.e. Grid, cloud or distributed computing while our work focus to make better use of existing resources within same environment.…”

Cognitive Intelligence

“…We have designed a visualizer, named Vishit, for visualizing texts in the Hindi language used in many states of India (Jain et al 2013(Jain et al , 2014. Finally, we have also carried out work on the parallel/distributed implementations of natural language processing systems on high performance computing platforms, such as multicore grid, cluster platforms Hadoop (Tomar et al 2013a(Tomar et al , 2013b(Tomar et al , 2014.…”