Language independent Transliteration mining system using Finite State Automata framework

We propose a Named Entities transliteration mining system using Finite State Automata (FSA). We compare the proposed approach with a baseline system that utilizes the Editex technique to measure the length-normalized phonetic based edit distance between the two words. We submitted three standard runs in NEWS2010 shared task and ranked first for English to Arabic (WM-EnAr) and obtained an Fmeasure of 0.915, 0.903, and 0.874 respectively.