Paper Info
Title
An unsupervised method for ranking translation words using a bilingual dictionary and wordnet
Abstract
In the context of machine translation, picking the correct translation for a target word among multiple candidates is an important process. In this paper, we propose an unsupervised method for ranking translation word selection for Korean verbs relying on only a bilingual Korean-English dictionary and WordNet. We focus on deciding which translation of the verb target word is the most appropriate by using a measure of inter-word semantic relatedness through the five extended relations between possible translations pair of target verb and some indicative noun clues. In order to reduce the weight of application of possibly unwanted senses for the noun translation, we rank the weight of possible senses for each noun translation word in advance. The evaluation shows that our method outperforms the default baseline performance and previous works. Moreover, this approach provides an alternative to the supervised corpus based approaches that rely on a large corpus of senses annotated data.
Year
DOI
Venue
2006
10.1007/11779568_94
IEA/AIE
Keywords
Field
DocType
noun translation word,bilingual dictionary,verb target word,indicative noun clue,target word,unsupervised method,ranking translation word selection,machine translation,target verb,noun translation,senses annotated data,correct translation,semantic relatedness,noun
Semantic similarity,Rule-based machine translation,Verb,Example-based machine translation,Bilingual dictionary,Computer science,Machine translation,Noun,Speech recognition,Artificial intelligence,Natural language processing,WordNet
Conference
Volume
ISSN
ISBN
4031
0302-9743
3-540-35453-0
Citations 
PageRank 
References 
0
0.34
9
Authors
3
Name
Order
Citations
PageRank
Kweon Yang Kim192.66
Se Young Park2474.28
Dong Kwon Hong300.68