Machine translation / by Pushpak Bhattacharyya.
By: Bhattacharyya, Pushpak [author.]
.
Contributor(s): Taylor and Francis
.
Material type: ![materialTypeLabel](/opac-tmpl/lib/famfamfam/BK.png)
![](/opac-tmpl/bootstrap/images/filefind.png)
![](/opac-tmpl/bootstrap/images/filefind.png)
![](/opac-tmpl/bootstrap/images/filefind.png)
![](/opac-tmpl/bootstrap/images/filefind.png)
![](/opac-tmpl/bootstrap/images/filefind.png)
![](/opac-tmpl/bootstrap/images/filefind.png)
![](/opac-tmpl/bootstrap/images/filefind.png)
![](/opac-tmpl/bootstrap/images/filefind.png)
![](/opac-tmpl/bootstrap/images/filefind.png)
![](/opac-tmpl/bootstrap/images/filefind.png)
![](/opac-tmpl/bootstrap/images/filefind.png)
![](/opac-tmpl/bootstrap/images/filefind.png)
chapter 1 Introduction -- chapter 2 Learning Bilingual Word Mappings -- chapter 3 IBM Model of Alignment -- chapter 4 Phrase-Based Machine Translation -- chapter 5 Rule-Based Machine Translation (RBMT) -- chapter 6 Example-Based Machine Translation.
Three paradigms have dominated machine translation (MT)-rule-based machine translation (RBMT), statistical machine translation (SMT), and example-based machine translation (EBMT) These paradigms differ in the way they handle the three fundamental processes in MT-analysis, transfer, and generation (ATG) In its pure form, RBMT uses rules, while SMT uses data. EBMT tries a combination-data supplies translation parts that rules recombine to produce translation.
There are no comments for this item.