| Abstract | We conduct experiments on data sets from the NEWS 2010 shared task on transliteration mining and achieve an F-measure of up to 92%, outperforming most of the semi-supervised systems that were submitted. |
| Experiments | For English/Arabic, English/Hindi and English/Tamil, our system is better than most of the semi-supervised systems presented at the NEWS 2010 shared task for transliteration mining. |
| Experiments | On the English/Russian data set, our system achieves 76% F-measure which is not good compared with the systems that participated in the shared task . |
| Experiments | The Wikipedia InterLanguage Links shared task data contains a much larger proportion of transliterations than a parallel corpus. |
| Introduction | We compare our unsupervised transliteration mining method with the semi-supervised systems presented at the NEWS 2010 shared task on transliteration mining (Kumaran et al., 2010) using four language pairs. |