Name
Abstract | ||
|---|---|---|
Abstract in machine translation, long sentences are usually assumed to be difficult to treat The main reason is the syntactic ambiguity which increases explosively as a sentence become longer Especially, in the machine translation using sentence patterns, a long sentence causes a critical coverage problem In this paper, we present a method of sentence partitioning which recognizes sub - sentence ranges by structure analysis, reducing the length of a sentence for translation For the analysis of the clausal structure, phrase - level sentence patterns which have only a little syntactic ambiguities are employed The structure analysis is conducted by the recognition of starting points of all clauses, dependency analysis, and depth analysis Then, the ranges of sub - sentences are extracted based on the depth by stages Our method was evaluated on 108 sentences extracted from CNN transcripts It showed 2% accuracy in the detection of simple sentences |
Year | Venue | Keywords |
|---|---|---|
2001 | NLPRS | dependence analysis,structure analysis,machine translation |
Field | DocType | Citations |
Structure analysis,Rule-based machine translation,Example-based machine translation,Computer science,Machine translation,Speech recognition,Transfer-based machine translation,Natural language processing,Artificial intelligence,Sentence | Conference | 6 |
PageRank | References | Authors |
1.03 | 2 | 4 |
Name | Order | Citations | PageRank |
|---|---|---|---|
| Yoon-Hyung Roh | 1 | 9 | 3.13 |
| Young Ae Seo | 2 | 8 | 3.88 |
| Ki-Young Lee | 3 | 14 | 4.05 |
| Sung-kwon Choi | 4 | 18 | 4.64 |