An English to Korean Transliteration Model of Extended Markov Window

An English to Korean Transliteration Model of Extended Markov Window SungYoung Jung SungLim Hong* Eunok Paek Information Technology Lab. LG Electronics Institute of Technology Seoul, Korea e-mail : {syjung, paek}@lg-elite.com * Present address: Sevice Engineering Team, Chollian Service Development Division, DACOM Corporation, Seoul, Korea (E-mail : syrup913@chollian.net) Abstract Automatic transliteration problem is to transcribe foreign words in one’s own alphabet. Machine generated transliteration can be useful in various applications such as indexing in an information retrieval system and pronunciation synthesis in a text-to-speech system. In this paper we present a model for statistical English- to-Korean transliteration that generates transliteration candidates with probability. The model is designed to utilize various information sources by extending a conventional Markov window. Also, an efficient and accurate method for alignment and syllabification of pronunciation units is described. The experimental results show a recall of 0.939 for trained words and 0.875 for untrained words when the best 10 candidates are considered. Introduction As the amount of international communication increases, more foreign words are flooding into the Korean language. Especially in the area of computer and information science, it has been reported that 29.4% of index terms are transliterated from or directly written in English in the case of a balanced corpus, KT-SET [18]. The transliteration of foreign words is indispensable in Korean language processing. In information retrieval, a simple method of processing foreign words is via query term translation based on a synonym dictionary of foreign words and their target transliteration. It is necessary to automate the construction process of a synonym dictionary since its maintenance requires continuous efforts for ever-incoming foreign words. Another area to which transliteration can be applied is a text-to-speech system where orthographic words are transcribed into phonetic symbols. In such applications, maximum likelihood [15], decision tree [1], neural network [10] or weighted finited-state acceptor [19] has been used for finding the best fit. English-to-Korean transliteration problem is that of generating an appropriate Korean word given an English word. In general, there can be various possible transliterations in Korean which correspond to a single English word. It is common that the newly imported foreign word is transliterated into several possible candidate words based on pronunciation, out of which only a few survive in competition over a period of time. In this respect, a statistical approach makes sense where multiple transliteration variations exist for one word, generating candidates in probable order. In this paper, we present a statistical method to transliterate English words in Korean alphabet to generate various candidates. In the next section, we describe a phonetic mapping table construction. In Section 2, we describe alignment and syllabification methods, and in Section 3, mathematical formulation for a statistical model is presented. Section 4 provides experimental results, and finally, we state our conclusions. 1 Phonetic mapping table construction First of all, we generate a mapping between English and Korean phonetic unit pairs (Table 6). In doing so, we use pronunciation symbols for English words (Table 5) as defined in the Oxford computer-usable dictionary …

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