Abstract

In this paper, we address the problem of mining transliterations of Named Entities (NEs) from large comparable corpora. We leverage the empirical fact that multilingual news articles with similar news content are rich in Named Entity Transliteration Equivalents (NETEs). Our mining algorithm, MINT, uses a cross-language document similarity model to align multilingual news articles and then mines NETEs from the aligned articles using a transliteration similarity model. We show that our approach is highly effective on 6 different comparable corpora between English and 4 languages from 3 different language families. Furthermore, it performs substantially better than a state-of-the-art competitor.

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