Paper: Hypothesis Selection in Machine Transliteration: A Web Mining Approach

ACL ID I08-1031
Title Hypothesis Selection in Machine Transliteration: A Web Mining Approach
Venue International Joint Conference on Natural Language Processing
Session Main Conference
Year 2008
Authors

We propose a new method of selecting hy- potheses for machine transliteration. We generate a set of Chinese, Japanese, and Ko- rean transliteration hypotheses for a given English word. We then use the set of translit- eration hypotheses as a guide to finding rel- evant Web pages and mining contextual in- formation for the transliteration hypotheses from the Web page. Finally, we use the mined information for machine-learning al- gorithms including support vector machines and maximum entropy model designed to select the correct transliteration hypothesis. In our experiments, our proposed method based on Web mining consistently outper- formed systems based on simple Web counts used in previous work, regardless of the lan- guage.