Syntactic parsing is vital for semantic treatment by many applications related to natural language processing (NLP), because form and content coincide in many cases. However, it has not yet reached the levels of reliable performance. By manually examining and analyzing individual machine translation output errors that involve syntax as well as semantics, this study attempts to discover what is ...

In the past decade, statistical machine translation (SMT) has been advanced from word-based SMT to phraseand syntax-based SMT. Although this advancement produces significant improvements in BLEU scores, crucial meaning errors and lack of cross-sentence connections at discourse level still hurt the quality of SMT-generated translations. More recently, we have witnessed two active movements in SM...

Machine translation translates a speech of text from the source language to target language. This paper introduces machine translation evaluation by calculating the semantic textual similarity between the machine translated sentences. The similarity score varies by using different values of alpha, beta and ranges semantic similarity [0,5]. The experiment is carried out on SemEval 2017 datasets....

Translating literary works is a difficult task, especially when it comes to cultural elements. It gets more difficult when words have ambiguities and multiple layers of meaning. The present study sought to examine the adequacy of witticism in the English renderings of Sa'di's clever remarks in Golistan (The Rose Garden). To this purpose, the researchers selected three English translations of Go...

Translating literary works is a difficult task, especially when it comes to cultural elements. It gets more difficult when words have ambiguities and multiple layers of meaning. The present study sought to examine the adequacy of witticism in the English renderings of Sa'di's clever remarks in Golistan (The Rose Garden). To this purpose, the researchers selected three English translations of Go...

Recently, Machine Translation (MT) has become a quite popular technology in everyday use through Web services such as Google Translate. Although the different MT approaches provide good results, none of them exploits contextual information like Named Entity (NE) to help user comprehension. In this paper, we present NERITS, a machine translation mashup system using semantic annotation from Wikim...

There are many cases in which the natural translation of one language into another results in a very different form than that of the original. The existence of translation divergences (i.e., crosslinguistic distinctions) makes the straightforward transfer from source structures into target structures impractical. Many existing translation systems have mechanisms for handling divergent structure...

Most information retrieval systems rely on the strict equality of terms between document and query in order to retrieve relevant documents to a given query. The term mismatch problem appears when users and documents’ authors use different terms to express the same meaning. Statistical translation models are proposed as an effective way to adapt language models in order to mitigate term mismatch...

In statistical machine translation (SMT), differences between domains of training and test data result in poor translations. Although there have been many studies on domain adaptation of language models and translation models, most require supervised in-domain language resources such as parallel corpora for training and tuning the models. The necessity of supervised data has made such methods d...

Language processing based on the semantic-oriented grammar First we focus on the semantic-oriented grammar, comparing this new approach with some existing approaches, whereby the differences become visible. As an application we describe a query system that is based on this new grammar. Finally, we sketch machine translation as the most important application.