Name
Abstract | ||
|---|---|---|
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 Wikimeta and Linked Open Data (LOD) provided by DBpedia. The goal of the application is to propose a cross-lingual translation by providing detailed information extracted from DBpedia about persons, locations and organizations in the mother tongue of the user. This helps at scaling the traditional multilingual task of machine translation to cross-lingual applications. |
Year | DOI | Venue |
|---|---|---|
2013 | 10.1007/978-3-642-41242-4_57 | Lecture Notes in Computer Science |
Keywords | Field | DocType |
Mashup,Machine Translation,Named Entity Recognition,Linked Open Data | Mashup,World Wide Web,Contextual information,Information retrieval,Computer science,Machine translation,Linked data,Exploit,Web service,Named-entity recognition,Comprehension | Conference |
Volume | ISSN | Citations |
7955 | 0302-9743 | 1 |
PageRank | References | Authors |
0.34 | 9 | 3 |
Name | Order | Citations | PageRank |
|---|---|---|---|
| Kamel Nebhi | 1 | 12 | 1.57 |
| luka nerima | 2 | 82 | 11.43 |
| eric wehrli | 3 | 29 | 6.20 |