A Kumaran and Tobias Kellner
Machine Transliteration deals with the conversion of text strings from one orthography to another, while preserving the phonetics of the strings in the two languages. Transliteration is an important problem in machine translation or crosslingual information retrieval, as most proper names and generic iconic terms are out-of-vocabulary words, and there-
fore need to be transliterated. There are numerous methods explored in the literature for machine transliteration, ranging from rule-based techniques to statistical learning tech-
niques. Here we focus our attention on language-independent techniques that potentially can scale well with a large number of languages. In this paper, we present a modular, sta-
tistical learning framework that lends itself for easy experimentation with transliteration tasks between a variety of different languages, in a language-independent manner. The
workbench includes a variety of components – algorithms, data-sets and transliterations scripts – for a quick assembly of an effective transliteration system across langauges. We
believe that such workbenches would be important in an increasingly multilingual world, for building systems that span a number of languages, quickly and effectively.
In the 30th annual international ACM SIGIR conference on Research and Development in Information Retrieval (SIGIR 2007), Amsterdam, Netherlands
Publisher Association for Computing Machinery, Inc.
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