Paper: MINT: A Method for Effective and Scalable Mining of Named Entity Transliterations from Large Comparable Corpora

ACL ID E09-1091
Title MINT: A Method for Effective and Scalable Mining of Named Entity Transliterations from Large Comparable Corpora
Venue Annual Meeting of The European Chapter of The Association of Computational Linguistics
Session Main Conference
Year 2009
Authors

In this paper, we address the problem of min- ing transliterations of Named Entities (NEs) from large comparable corpora. We leverage the empirical fact that multilingual news ar- ticles 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.