Machine transliteration is a method for automatically converting words in one language into phonetically equivalent ones in another language. Machine transliteration plays an important role in natural language applications such as information retrieval and machine translation, especially for handling proper nouns and technical terms. Four machine transliteration models - grapheme-based transliteration model, phoneme-based transliteration model, hybrid transliteration model, and correspondence-based transliteration model - have been proposed by several researchers. To date, however, there has been little research on a framework in which multiple transliteration models can operate simultaneously. Furthermore, there has been no comparison of the four models within the same framework and using the same data. We addressed these problems by 1) modeling the four models within the same framework, 2) comparing them under the same conditions, and 3) developing a way to improve machine transliteration through this comparison. Our comparison showed that the hybrid and correspondence-based models were the most effective and that the four models can be used in a complementary manner to improve machine transliteration performance.
Transliterating words and names from one language to another is a frequent and highly productive phenomenon. Transliteration is information loosing since important distinctions are not preserved in the process. Hence, automatically converting transliterated words back into their original form is a real challenge. However, due to wide applicability in MT and CLIR, it is a computationally interesting problem. Previously proposed back-transliteration methods are based either on phoneme modeling or grapheme modeling across languages. In this paper, we propose a new method, combining the two models in order to enhance the back–transliterations of words transliterated in Japanese. Our experiments show that the resulting system outperforms single-model systems.  Named entity phrases are some of the most difficult phrases to translate because new phrases can appear from nowhere, and because many are domain specific, not to be found in bilingual dictionaries. We present a novel algorithm for translating named entity phrases using easily obtainable monolingual and bilingual resources. We report on the application and evaluation of this algorithm in translating Arabic named entities to English. We also compare our results with the results obtained from human translations and a commercial system for the same task. There is increasing concern about English-Korean (E-K) transliteration recently. In the previous works, direct converting methods from English alphabets to Korean alphabets were a main research topic. In this paper, we present an E-K transliteration model using pronunciation and contextual rules. Unlike the previous works, our method uses phonetic information such as phoneme and its context. We also use word formation information such as English words of Greek origin, With them, our method shows significant performance increase about 31 in word accuracy.