Abstract
Foreign name translations typically include multiple spelling variants. These variants cause data sparseness problems, increase Out-of-Vocabulary (OOV) rate, and present challenges for machine translation, information extraction and other NLP tasks. This paper aims to identify name spelling variants in the target language using the source name as an anchor. Based on word-to-word translation and transliteration probabilities, as well as the string edit distance metric, target name translations with similar spellings are clustered. With this approach tens of thousands of high precision name translation spelling variants are extracted from sentence-aligned bilingual corpora. When these name spelling variants are applied to Machine Translation and Information Extraction tasks, improvements over strong baseline systems are observed in both cases.
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