Name deciphering in unrelated languages: The case study of Farsi and English

In this work we propose an unsupervised model for deciphering names in two unrelated languages, English and Farsi. The proposed model is a generative non-parametric model that is a customized version of [3] model for name extraction. We show that this unsupervised model is able to achieve competitive results in comparison with a supervised model. Although the accuracy of the unsupervised model is lower than the supervised model, using this model makes it possible to produce list of parallel names without parallel corpora.