Identifying Language Origin of Person Names With N-Grams of Different Units
Yining Chen, Jiali You, Min Chu, Yong Hui Zhao, Jinlin Wang · 2006
Identifying the language origin of a name in English is important for generating its correct pronunciation. In this paper, N-grams of syllable-based letter clusters are proposed for the task. The performance of the N-gram model of a set of frequently used letter clusters (correspond to syllables) is compared to that of letter N-gram model in a four-language task: English, German, French, and Portuguese. On average, the letter cluster N-gram, which has 26% error rate, is slightly better than the letter N-gram, which has 27.2% error rate. Furthermore, it is found that the error distributions from the two N-grams have fairly large differences. Therefore, AdaBoost is used to combine the results from N-grams of different units. The error rate is reduced to 22.5% or a relative 17.5% error reduction is achieved after the combination