Unsupervised constraint driven learning for transliteration discovery
Ming‐Wei Chang, Dan L. Goldwasser, Dan Roth, Yuancheng Tu · 2009
This paper introduces a novel unsupervised constraint-driven learning algorithm for identifying named-entity (NE) transliterations in bilingual corpora.The proposed method does not require any annotated data or aligned corpora.Instead, it is bootstrapped using a simple resource -a romanization table.We show that this resource, when used in conjunction with constraints, can efficiently identify transliteration pairs.We evaluate the proposed method on transliterating English NEs to three different languages -Chinese, Russian and Hebrew.Our experiments show that constraint driven learning can significantly outperform existing unsupervised models and achieve competitive results to existing supervised models.