Machine transliteration using SVM and HMM
by Soma Chatterjee; Kamal Sarkar
International Journal of Advanced Intelligence Paradigms (IJAIP), Vol. 19, No. 1, 2021

Abstract: Name transliteration plays an important role in developing automatic machine translation and cross lingual information retrieval system because these systems cannot directly translate out-of-vocabulary (OOV) words. In this article, a SVM-based name transliteration approach has been presented. This approach considers transliteration task as a multi-class problem of pattern classification, where the input is a source transliteration unit (chunks of source grapheme) and the classes are the distinct transliteration units (chunks of target grapheme) in the target language. A study on using hidden Markov model (HMM) for solving machine transliteration problem viewed as a sequence learning problem has also been presented in this paper. Bengali-to-English forward and backward name transliteration have been considered in this study. Our proposed methods have been compared with some existing transliteration method that uses a modified version of joint-source channel model. After the systems have been evaluated, the obtained results show that our proposed SVM-based model gives the best results among the others. Our experiments also reveal that the performance of HMM-based system is comparable with the SVM-based system.

Online publication date: Wed, 28-Apr-2021

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