Classifier based text simplification for improved machine translation

Machine Translation is one of the research fields of Computational Linguistics. The objective of many MT Researchers is to develop an MT System that produce good quality and high accuracy output translations and which also covers maximum language pairs. As internet and Globalization is increasing da...

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Bibliographic Details
Published in2015 International Conference on Advances in Computer Engineering and Applications (ICACEA) pp. 46 - 50
Main Authors Tyagi, Shruti, Chopra, Deepti, Mathur, Iti, Joshi, Nisheeth
Format Conference Proceeding
LanguageEnglish
Published IEEE 01.03.2015
Subjects
Computational linguistics
Computers
Machine Translation
Naïve Bayes Classifier
Pragmatics
Root mean square
Support Vector Machine Classifier
Support vector machines
Syntactics
Text Simplification
Training
Online AccessGet full text
DOI10.1109/ICACEA.2015.7164711

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Summary:Machine Translation is one of the research fields of Computational Linguistics. The objective of many MT Researchers is to develop an MT System that produce good quality and high accuracy output translations and which also covers maximum language pairs. As internet and Globalization is increasing day by day, we need a way that improves the quality of translation. For this reason, we have developed a Classifier based Text Simplification Model for English-Hindi Machine Translation Systems. We have used support vector machines and Naïve Bayes Classifier to develop this model. We have also evaluated the performance of these classifiers.
DOI:10.1109/ICACEA.2015.7164711