Name
Abstract | ||
|---|---|---|
This paper presents an approach of target word selection for Korean verbs based on lexical knowledge contained in a Korean-English bilingual dictionary and WordNet. We focus on deciding which translation of the target word is the most appropriate using the measure of semantic relatedness between possible translations of target word and some indicative clue words. With five Korean ambiguous verbs, we report an average accuracy of 51% that outperforms the default baseline performance and previous works. |
Year | DOI | Venue |
|---|---|---|
2005 | 10.1007/11589990_177 | Australian Conference on Artificial Intelligence |
Keywords | Field | DocType |
korean ambiguous verb,possible translation,korean verb,lexical knowledge,average accuracy,target word,default baseline performance,target word selection,korean-english bilingual dictionary,indicative clue word,semantic relatedness | Semantic similarity,Verb,Korean verbs,Bilingual dictionary,Computer science,Multilingualism,Natural language processing,Artificial intelligence,Knowledge base,WordNet,Semantics | Conference |
Volume | ISSN | ISBN |
3809 | 0302-9743 | 3-540-30462-2 |
Citations | PageRank | References |
0 | 0.34 | 6 |
Authors | ||
3 | ||
Name | Order | Citations | PageRank |
|---|---|---|---|
| Kweon Yang Kim | 1 | 9 | 2.66 |
| Byong Gul Lee | 2 | 0 | 0.34 |
| Dong Kwon Hong | 3 | 0 | 0.68 |