Learning to find transliteration on the Web

This prototype demonstrate a novel method for learning to find transliterations of proper nouns on the Web based on query expansion aimed at maximizing the probability of retrieving translit-erations from existing search engines. Since the method we used involves learning the morphologi-cal relationships between names and their translit-erations, we refer to this IR-based approach as morphological query expansion for machine trans-literation. The morphological query expansion approach is general in scope and can be applied to translation and transliteration, but we focus on transliteration in this paper. Many texts containing proper names (e.g., “The cities of Mesopotamia prospered under Parthian and Sassanian rule.”) are submitted to machine translation services on the Web every day, and there are also service on the Web specifically tar-get transliteration of proper names, includin