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An optimized cognitive-assisted machine translation approach for natural language processing

  • Abdulaziz AlarifiEmail author
  • Ayed Alwadain
Article

Abstract

Currently, computer-aided machine translation (MT) processes play a significant role in natural language processing used to translate a specified language into another language like English to Spanish, Latin to French. During the translation process, and particularly during phrase composition, MT systems may exhibit several issues, including failure to produce high quality translations, increased time consumption, diminished linguistic precision and complexity. This research introduces an optimized cognitive assisted statistical MT process that is intended to reduce these difficulties. This process uses a supervised machine learning technique (OCSMT-SMT) for natural language processing that is intended to translate phrases with a higher degree of precision than other Support Vector Machine, Linear Regression, Decision Trees, Naïve Bayes, and K-Nearest Neighbor MT techniques. The method introduced here uses semantic operations to examine the collected messages that are processed in network and obtained results have been stored in the memory to get the exact translation which helps to learn phrase semantics using MT. The OCSMT-SMT approach enables smarter, faster decision making regarding phrase translations, which greatly reduces translation time. The efficiency of this approach is evaluated using the bilingual evaluation understudy (BLEU) and Better Evaluation as Ranking (BEER) metrics for English language phrase datasets in English language. This ensures the high precision while performing the MT process.

Keywords

Computer aided machine translation Cognitive assisted statistical machine translation BLEU BEER Cognitive machine learning 

Mathematics Subject Classification

01-00 65Exx 62-07 62-09 86-05 86-03 

Notes

Acknowledgements

The authors extend their appreciation to the Deanship of Scientific Research at King Saud University for funding this work through Research Group No. RG-1440-078.

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Copyright information

© Springer-Verlag GmbH Austria, part of Springer Nature 2019

Authors and Affiliations

  1. 1.Computer Science Department, Community CollegeKing Saud UniversityRiyadhSaudi Arabia

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