"Named Entity Translations, Offline or Online?"
Fei Huang   

Named Entity(NE) translation is both semantically important and technically 
challenging, because OOV words occur frequently in person, location or 
organization names. High accuracy NE translation can contribute to multilingual 
natural language processing, such as statistical machine translation, cross 
information retrieval and crosslingual information extraction. In this talk I 
will present both offline and online methods for NE translation.

In offline NE translation, starting from a bilingual corpus where NEs are 
automatically tagged for each language, NE pairs are aligned in order to minimize 
the overall multi-feature alignment cost.  An NE transliteration model is presented 
and iteratively trained using named entity pairs extracted from a bilingual 
dictionary. The transliteration cost, combined with the named entity tagging cost 
and word-based translation cost, constitute the multi-feature alignment cost. These 
features are derived from several information sources using unsupervised and partly 
supervised methods.  A greedy search algorithm is applied to minimize the alignment 
cost. Experiments show that the proposed approach extracts NE translingual equivalence 
with 81% F-score and improves the translation score from 7.68 to 7.74.

In online NE translation, a bunch of topic relevant documents (wrt the translation 
hypothesis) are retrieved, and NEs in these documents are extracted, then matched 
aganist NEs in the source translation document. Those NE pairs with minimum 
transliteration cost are considered as translation equivalence, which further increase 
the translation quality from 7.87 to 7.96.