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Author Costa-Juss, Marta R ♦ Farrs, Mireia
Source ACM Digital Library
Content type Text
Publisher Association for Computing Machinery (ACM)
File Format PDF
Copyright Year ©2014
Language English
Subject Domain (in DDC) Computer science, information & general works ♦ Data processing & computer science
Subject Keyword Lexis ♦ Linguistics ♦ Morphology ♦ Orthography ♦ Semantics ♦ Statistical machine translation ♦ Syntax
Abstract Machine translation can be considered a highly interdisciplinary and multidisciplinary field because it is approached from the point of view of human translators, engineers, computer scientists, mathematicians, and linguists. One of the most popular approaches is the Statistical Machine Translation (smt) approach, which tries to cover translation in a holistic manner by learning from parallel corpus aligned at the sentence level. However, with this basic approach, there are some issues at each written linguistic level (i.e., orthographic, morphological, lexical, syntactic and semantic) that remain unsolved. Research in smt has continuously been focused on solving the different linguistic levels challenges. This article represents a survey of how the smt has been enhanced to perform translation correctly at all linguistic levels.
ISSN 03600300
Age Range 18 to 22 years ♦ above 22 year
Educational Use Research
Education Level UG and PG
Learning Resource Type Article
Publisher Date 2014-01-01
Publisher Place New York
e-ISSN 15577341
Journal ACM Computing Surveys (CSUR)
Volume Number 46
Issue Number 3
Page Count 28
Starting Page 1
Ending Page 28


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Source: ACM Digital Library