Name
Abstract | ||
|---|---|---|
We introduce a method for learning to find domain-specific translations for a given term on the Web. In our approach, the source term is transformed into an expanded query aimed at maximizing the probability of retrieving translations from a very large collection of mixed-code documents. The method involves automatically generating sets of target- language words from training data in specific domains, automatically selecting target words for effectiveness in retrieving documents containing the sought-after translations. At run time, the given term is transformed into an expanded query and submitted to a search engine, and ranked translations are extracted from the document snippets returned by the search engine. We present a prototype, TermMine, which applies the method to a Web search engine. Evaluations over a set of domains and terms show that TermMine outperforms state-of-the-art machine translation systems. |
Year | Venue | Keywords |
|---|---|---|
2008 | AMTA | machine translation,source term,search engine,web search engine |
DocType | Citations | PageRank |
Conference | 0 | 0.34 |
References | Authors | |
14 | 4 | |
Name | Order | Citations | PageRank |
|---|---|---|---|
| Jian-Cheng Wu | 1 | 70 | 13.30 |
| Peter Wei-Huai Hsu | 2 | 0 | 0.34 |
| Chiung-Hui Tseng | 3 | 1 | 0.69 |
| Jason S. Chang | 4 | 345 | 62.64 |