![]() Oh and Choi develped a phoneme based model using rule based approach incorporating phonetics as an intermediate repre- sentation. There are three different machine transliteration develop- ments in the year 2000, from three separate research team. International Journal of Scientific & Engineering Research, Volume 2, Issue 12, December-2011 2 This approach was adapted by Stalls and Knight for back transliteration from Arabic to English. Tical based approach proposed by Knight and Graehl in 1998įor back transliteration from English to Japanese Katakana. The next development in transliteration was based on a statis. The pro- posed neural network and knowledge-based hybrid system generate multiple English spellings for Arabic person names. Contributors to Machine Transliteration The very first attempt in transliteration was done by Arababi through a combination of neural network and expert systems for transliterating from Arabic-English in 1994. The figure 1 shows the researchers who proposed different approaches to develop various machine transliteration sys- tems. 1.1 Major Contribution to Machine Transliteration The transliteration model should be design while considering all these complexi- ties. Likewise, “sha” in Kannada (as in Roshan) and “tio” in English (as in ration) sound similar. For example, “ph” and “f” both map to the same sound of (f). For transliterating names, we have to exploit the pho- netic correspondence of alphabets and sub-strings in English to Kannada. Also on the basis of its context, consonants like „c‟, ‟d‟, ‟l‟, or „n‟, has multiple transliterations in Kannada lan. This is because vowels in English may correspond to long vowels or short vowels or some time combination of vowels in Kannada during trans- literation. Similarly „i‟ can be transliterated either „i‟ or „ai‟ on the basis of its context. For example, the English (source) grapheme „a‟ can be transliterated into Kan- nada (target) language graphemes on the basis of its context, like „a‟, ‟aa‟, „ei‟ etc. Transliteration usually depends on context. Such model requires considerable knowledge of the languages. Most of the current transliteration systems use a generative model based on alignment for transliteration and consider the task of generat- ing an appropriate transliteration for a given word. Various methodol- ogies have been developed for machine transliteration based on the nature of the languages considered. The topic of machine transliteration has been studied exten- sively for several different language pairs. Phonetic structure of words should be preserve as closely as possible. The transliteration model must be design in such a way that the Machin e transliteration can play an im- portant role in natural language application such as informa- tion retrieval and machine translation, especially for handling proper nouns and technical terms, cross-language applica- tions, data mining and information retrieval system. Machine transliteration is the practice of transcribing a charac- ter or word written in one alphabetical system into another alphabetical system. ![]() Index Terms - Named Entity, Agglutinative, Natural Language Processing, Transliteration, Dravidian Languages This paper is intended to give a brief survey on transliteration for Indianlanguages. Literature shows that, recently some recognizable attempts have done for few Indian languages like Hindi, Bengali, Telugu, Kannada and Tamil languages. Even though a number of different transliteration mechanisms are available for worlds top level languages like English, European languages, Asian languages like Chinese, Japanese, Korean and Arabic, still it is an initial stage for Indian languages. Machine transliteration is an important NLP tool required mainly for translating named entities from one language to another. International Journal of Scientific & Engineering Research, Volume 2, Issue 12, December-2011 1Ībstract - This paper address the various developments in Indian language machine transliteration system, which is considered as a very important task needed for many natural language processing (NLP) applications. Machine Transliteration for Indian Languages: A Literature Survey ![]()
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