Predicting correctness of “Google translate”

Yulia Rossikova, J. Jenny Li, Patricia Morreale

Research output: Chapter in Book/Report/Conference proceedingConference contributionpeer-review

Abstract

This paper presents a new modeless approach for Machine Learning predictions, called Radius of Neighbors (RN). We applied RN to predict the correctness of Google translator and found it to be an improvement over K-Nearest Neighbors (KNN) in terms of prediction accuracy. Both methods are applicable to situations when a mathematical prediction model does not exist or is unknown. With RN, we will be able to create new applications that rely on the users' awareness of translation accuracy, e.g. an online instant messager, which allows users to chat in various natural languages.

Original languageEnglish
Title of host publicationProceedings of the 2015 International Conference on Artificial Intelligence, ICAI 2015 - WORLDCOMP 2015
EditorsDavid de la Fuente, Roger Dziegiel, Elena B. Kozerenko, Peter M. LaMonica, Raymond A. Liuzzi, Jose A. Olivas, Todd Waskiewicz, George Jandieri, Hamid R. Arabnia
PublisherCSREA Press
Pages825-826
Number of pages2
ISBN (Electronic)1601324073, 9781601324078
StatePublished - 2019
Event2015 International Conference on Artificial Intelligence, ICAI 2015 - WORLDCOMP 2015 - Las Vegas, United States
Duration: 27 Jul 201530 Jul 2015

Publication series

NameProceedings of the 2015 International Conference on Artificial Intelligence, ICAI 2015 - WORLDCOMP 2015

Conference

Conference2015 International Conference on Artificial Intelligence, ICAI 2015 - WORLDCOMP 2015
Country/TerritoryUnited States
CityLas Vegas
Period27/07/1530/07/15

Keywords

  • K-Nearest Neighbors (KNN)
  • Machine Learning
  • Prediction

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