Machine transliteration is a method for automatically converting words in one language into phonetically equivalent ones in another language. Machine transliteration plays an important role in natural language applications such as information retrieval and machine translation, especially for handling proper nouns and technical terms. Four machine transliteration models - grapheme-based transliteration model, phoneme-based transliteration model, hybrid transliteration model, and correspondence-based transliteration model - have been proposed by several researchers. To date, however, there has been little research on a framework in which multiple transliteration models can operate simultaneously. Furthermore, there has been no comparison of the four models within the same framework and using the same data. We addressed these problems by 1) modeling the four models within the same framework, 2) comparing them under the same conditions, and 3) developing a way to improve machine transliteration through this comparison. Our comparison showed that the hybrid and correspondence-based models were the most effective and that the four models can be used in a complementary manner to improve machine transliteration performance.
Machine transliteration has received significant research attention in recent years. In most cases, the source language and target language have been English and an Asian language, respectively -- for example, English to Japanese @cite_2 , English to Chinese @cite_18 @cite_31 , and English to Korean @cite_19 @cite_24 @cite_37 @cite_40 @cite_27 @cite_13 @cite_8 @cite_34 @cite_35 . In this section, we review previous work related to the four transliteration models.
@cite_35 @cite_18 @cite_37 @cite_8 @cite_24 @cite_19 @cite_40 @cite_27 @cite_2 @cite_31 @cite_34 @cite_13
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There is increasing concern about English-Korean (E-K) transliteration recently. In the previous works, direct converting methods from English alphabets to Korean alphabets were a main research topic. In this paper, we present an E-K transliteration model using pronunciation and contextual rules. Unlike the previous works, our method uses phonetic information such as phoneme and its context. We also use word formation information such as English words of Greek origin, With them, our method shows significant performance increase about 31 in word accuracy. We have developed a technique for automatic transliteration of named entities for English-Chinese cross-language spoken document retrieval (CL-SDR). Our retrieval system integrates machine translation, speech recognition and information retrieval technologies. An English news story forms a textual query that is automatically translated into Chinese words, which are mapped into Mandarin syllables by pronunciation dictionary lookup. Mandarin radio news broadcasts form spoken documents that are indexed by word and syllable recognition. The information retrieval engine performs matching in both word and syllable scales. The English queries contain many named entities that tend to be out-of-vocabulary words for machine translation and speech recognition, and are omitted in retrieval. Names are often transliterated across languages and are generally important for retrieval. We present a technique that takes in a name spelling and automatically generates a phonetic cognate in terms of Chinese syllables to be used in retrieval. Experiments show consistent retrieval performance improvement by including the use of named entities in this way. Many foreign words and English words appear in Korean texts, especially in the areas of science and engineering. We recognize two issues related to foreign words, which should be addressed for information retrieval (IR). First, since foreign words are introduced dynamically and not always found in a dictionary, they cause problems in morphological analysis required for indexing. Second, although a foreign word and its origin in the source language like English refer to the same concept, they are erroneously treated as independent index terms. As a way of alleviating the first problem we developed an algorithm that first identifies a phrase containing a foreign word and then extracts the foreign word part from the phrase based on statistical information. For the second problem, we present our method for back-transliteration of a foreign word to its English origin. Finally we report our evaluation results for each of the algorithms and experimental results for their impact on IR effectiveness. We present in this paper the method of English-to-Korean (E-K) transliteration and back-transliteration. In Korean technical documents, many English words are transliterated into Korean words in various forms in diverse ways. As English words and Korean transliterations are usually technical terms and proper nouns, it is hard to find a transliteration and its variations in a dictionary. Therefore an automatic transliteration system is needed to find the transliterations of English words without manual intervention.To explain E-K transliteration phenomena, we use phoneme chunks that do not have a length limit. By applying phoneme chunks, we combine different length information with easy. The E-K transliteration method has three steps. In the first, we make a phoneme network that shows all possible transliterations of the given word. In the second step, we apply phoneme chunks, extracted from training data, to calculate the reliability of each possible transliteration. Then we obtain probable transliterations of the given English word. Pharmaceutical compositions free from pathogenic microorganisms which could be harmful to warm-blooded animals, are provided containing, as an active ingredient, an antimicrobially effective amount of at least one compound of the formula (I) in which R represents hydrogen, -CO-R1 or -SO2-R2, wherein R1 represents optionally substituted alkyl, alkenyl or alkinyl, optionally substituted phenyl, optionally substituted phenoxyalkyl, phenylalkyl, cycloalkyl, alkylamino, dialkylamino or optionally substituted phenylamino, R2 represents alkyl or optionally substituted phenyl, A represents a keto group or a -CH(OH)- grouping, X represents hydrogen or an -OR grouping, wherein R is as defined above, X1 represents alkyl or optionally substituted phenyl, Y represents -CH- or a nitrogen atom, Z represents halogen, alkyl, halogenoalkyl, cycloalkyl, alkoxy, alkythio, alkoxycarbonyl, optionally substituted phenyl, optionally substituted phenoxy, optionally substituted phenylalkyl, amino, cyano or nitro and n represents 0 or an integer from 1 to 5, or a salt thereof, in admixture with a sterile pharmaceutical carrier, such as a solid or liquefied gaseous diluent, or with a liquid diluent other than a solvent of molecular weight less than 200 (preferably 300) except in the presence of a surface-active agent. The compositions of the invention are useful as antimycotic agents. Also included in the invention is the provision of the compositions of the invention in unit dosage form as well as the provision of methods of treatment wherein the compositions of the invention are administered to warm-blooded animals. In Korean technical documents, many English words are transliterated into Korean in various ways. Most of these words are technical terms and proper nouns that are frequently used as query terms in information retrieval systems. As the communication with foreigners increases, an automatic transliteration system is needed to find the various transliterations for the cross lingual information systems, especially for the proper nouns and technical terms which are not registered in the dictionary. In this paper, we present a language independent Statistical Transliteration Model (STM) that learns rules automatically from word-aligned pairs in order to generate transliteration variations. For the transliteration from English to Korean, we compared two methods based on STM: the pivot method and the direct method. In the pivot method, the transliteration is done in two steps: converting English words into pronunciation symbols by using the STM and then converting these symbols into Korean words by using the Korean standard conversion rule. In the direct method, English words are directly converted to Korean words by using the STM without intermediate steps. After comparing the performance of the two methods, we propose a hybrid method that is more effective to generate various transliterations and consequently to retrieve more relevant documents. 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. Most foreign names are transliterated into Chinese, Japanese or Korean with approximate phonetic equivalents. The transliteration is usually achieved through intermediate phonemic mapping. This paper presents a new framework that allows direct orthographical mapping (DOM) between two different languages, through a joint source-channel model, also called n-gram transliteration model (TM). With the n-gram TM model, we automate the orthographic alignment process to derive the aligned transliteration units from a bilingual dictionary. The n-gram TM under the DOM framework greatly reduces system development effort and provides a quantum leap in improvement in transliteration accuracy over that of other state-of-the-art machine learning algorithms. The modeling framework is validated through several experiments for English-Chinese language pair. Automatic transliteration and back-transliteration across languages with drastically different alphabets and phonemes inventories such as English Korean, English Japanese, English Arabic, English Chinese, etc, have practical importance in machine translation, crosslingual information retrieval, and automatic bilingual dictionary compilation, etc. In this paper, a bi-directional and to some extent language independent methodology for English Korean transliteration and back-transliteration is described. Our method is composed of character alignment and decision tree learning. We induce transliteration rules for each English alphabet and back-transliteration rules for each Korean alphabet. For the training of decision trees we need a large labeled examples of transliteration and backtransliteration. However this kind of resources are generally not available. Our character alignment algorithm is capable of highly accurately aligning English word and Korean transliteration in a desired way.