Hypothesis Selection in Machine Transliteration: A Web Mining Approach
Jong–Hoon Oh, Hitoshi Isahara · International Joint Conference on Natural Language Processing · 2008
We propose a new method of selecting hypotheses for machine transliteration. We generate a set of Chinese, Japanese, and Korean transliteration hypotheses for a given English word. We then use the set of transliteration hypotheses as a guide to finding relevant Web pages and mining contextual information for the transliteration hypotheses from the Web page. Finally, we use the mined information for machine-learning algorithms 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 outperformed systems based on simple Web counts used in previous work, regardless of the language.