期刊名称：Conference on European Chapter of the Association for Computational Linguistics (EACL)
出版年度：2009
卷号：2009
出版社：ACL Anthology
摘要：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.
