Classifier based text simplification for improved machine translation

• Published in: 2015 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
• Language: English
• 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
• DOI: 10.1109/ICACEA.2015.7164711

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.