Machine Translation is one of the research fields of Computational Linguistics.
Structures which can facilitate open world assumptions and can be flexible enough to incorporate and recognize more than one name for an entity.
There are various methods for estimating the quality of output sentences, but in this paper we focus on Na\"ive Bayes classifier to build model using features which are extracted from the input sentences.
In this paper, we show an approach which can provide automatic ranks to MT outputs (translations) taken from different MT Engines and which is based on N-gram approximations.
This paper presents a novel approach to machine translation by combining the state of art name entity translation scheme.
Part-of-speech (POS) tagging is a process of assigning the words in a text corresponding to a particular part of speech.
This paper considers the problem for estimating the quality of machine translation outputs which are independent of human intervention and are generally addressed using machine learning techniques. There are various measures through which a machine learns translations quality.
Proper transliteration of name entities plays a very significant role in improving the quality of machine translation.
Machine Translation for Indian languages is an emerging research area.
We live in a translingual society, in order to communicate with people from different parts of the world we need to have an expertise in their respective languages.