We propose a language-independent method for the automatic extraction of transliteration pairs from parallel corpora .

We also apply our method to English/Hindi and English/Arabic parallel corpora and compare the results with manually built gold standards which mark transliterated word pairs.

We evaluate our transliteration mining algorithm on three tasks: transliteration mining from Wikipedia InterLanguage Links, transliteration mining from parallel corpora , and word alignment using a word aligner with a transliteration component.

In the evaluation on parallel corpora , we compare our mining results with a manually built gold standard in which each word pair is either marked as a transliteration or as a non-transliteration.

4.2 Experiments Using Parallel Corpora

In this section, we present an iterative method for the extraction of transliteration pairs from parallel corpora which is fully unsuperVised and language pair independent.

Transliteration mining on the WIL data sets is easier due to a higher percentage of transliterations than in parallel corpora .

We also do experiments on parallel corpora for two language pairs.

The transliteration module is trained on the transliteration pairs which our mining method extracts from the parallel corpora .

Using automatically obtained word clusters instead of POS tags yields essentially the same results, thus making our methods applicable to all languages pairs with parallel corpora , whether syntactic resources are available for them or not.

Several techniques have been recently proposed to automatically identify and estimate parameters for PSCFGS (or related synchronous grammars) from parallel corpora (Galley et al., 2004; Chiang, 2005; Zollmann and Venugopal, 2006; Liu et al., 2006; Marcu et al., 2006).

In this work we experiment with PSCFGs that have been automatically learned from word-aligned parallel corpora .