DeepAI AI Chat
Log In Sign Up

Semantic Web for Machine Translation: Challenges and Directions

07/23/2019
by   Diego Moussallem, et al.
0

A large number of machine translation approaches have recently been developed to facilitate the fluid migration of content across languages. However, the literature suggests that many obstacles must still be dealt with to achieve better automatic translations. One of these obstacles is lexical and syntactic ambiguity. A promising way of overcoming this problem is using Semantic Web technologies. This article is an extended abstract of our systematic review on machine translation approaches that rely on Semantic Web technologies for improving the translation of texts. Overall, we present the challenges and opportunities in the use of Semantic Web technologies in Machine Translation. Moreover, our research suggests that while Semantic Web technologies can enhance the quality of machine translation outputs for various problems, the combination of both is still in its infancy.

READ FULL TEXT

page 1

page 2

page 3

page 4

11/26/2017

Machine Translation Using Semantic Web Technologies: A Survey

A large number of machine translation approaches has been developed rece...
07/09/2021

Using Machine Translation to Localize Task Oriented NLG Output

One of the challenges in a task oriented natural language application li...
12/15/2016

Building a robust sentiment lexicon with (almost) no resource

Creating sentiment polarity lexicons is labor intensive. Automatically t...
11/02/2020

The 2020s Political Economy of Machine Translation

This paper explores the hypothesis that the diversity of human languages...
09/24/2014

Semantically-Informed Syntactic Machine Translation: A Tree-Grafting Approach

We describe a unified and coherent syntactic framework for supporting a ...