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Machine transliteration has received significant attention as a supporting tool for machine translation and cross-language information retrieval. During the last decade, four kinds of transliteration model have been studied — grapheme-based model, phoneme-based model, hybrid model, and correspondence-based model. These models are classified in terms of the information sources for transliteration or the units to be transliterated — source graphemes, source phonemes, both source graphemes and source phonemes, and the correspondence between source graphemes and phonemes, respectively. Although each transliteration model has shown relatively good performance, one model alone has limitations on handling complex transliteration behaviors. To address the problem, we combined different transliteration models with a “generating transliterations followed by their validation” strategy. The strategy makes it possible to consider complex transliteration behaviors using the strengths of each model and to improve transliteration performance by validating transliterations. Our method makes use of web-based and transliteration model-based validation for transliteration validation. Experiments showed that our method outperforms both the individual transliteration models and previous work.

Transliteration pair acquisition has received significant attention as a technique for constructing up-to-date transliteration lexicons, and for supporting machine translation and cross-language information retrieval. Previous studies on transliteration pair acquisition focused on only the phonetic similarity model but seldom considered the real-usage of transliterations in texts. Moreover, previous web-based validation models considered only one-way validation (validation from the viewpoint of a source term) rather than joint validation between a source term and a target term. To address these problems, we propose a novel transliteration pair acquisition model that extracts transliteration pairs from the Web and validates the pairs by combining the phonetic similarity and joint web-validation models. Experiments demonstrated that our transliteration pair acquisition model was effective.

Several models for transliteration pair acquisition have been proposed to overcome the out-of-vocabulary problem caused by transliterations To date, however, there has been little literature regarding a framework that can accommodate several models at the same time Moreover, there is little concern for validating acquired transliteration pairs using up-to-date corpora, such as web documents To address these problems, we propose a hybrid model for transliteration pair acquisition In this paper, we concentrate on a framework for combining several models for transliteration pair acquisition Experiments showed that our hybrid model was more effective than each individual transliteration pair acquisition model alone.

Machine transliteration is an automatic method to generate characters or words in one alphabetical system for the corresponding characters in another alphabetical system. There has been increasing concern on machine transliteration as an assistant of machine translation and information retrieval. Three machine transliteration models, including “grapheme-based model”, “phoneme-based model”, and “hybrid model”, have been proposed. However, there are few works trying to make use of correspondence between source grapheme and phoneme, although the correspondence plays an important role in machine transliteration. Furthermore there are few works, which dynamically handle source grapheme and phoneme. In this paper, we propose a new transliteration model based on an ensemble of grapheme and phoneme. Our model makes use of the correspondence and dynamically uses source grapheme and phoneme. Our method shows better performance than the previous works about 15~23% in English-to-Korean transliteration and about 15~43% in English-to-Japanese transliteration.

Many optimizing problems can be interpreted as the problem of minimizing the cost functions in the form of the spin-glass Hamiltonian, i.e. $$H = - \sum\limits_{i,j} {{J_{ij}}} {s_i}{s_j}.$$ (1)