{"id":658695,"date":"2020-05-13T12:00:25","date_gmt":"2020-05-13T19:00:25","guid":{"rendered":"https:\/\/www.microsoft.com\/en-us\/research\/?post_type=msr-research-item&p=658695"},"modified":"2020-05-13T12:00:25","modified_gmt":"2020-05-13T19:00:25","slug":"language-independent-transliteration-mining-system-using-finite-state-automata-framework","status":"publish","type":"msr-research-item","link":"https:\/\/www.microsoft.com\/en-us\/research\/publication\/language-independent-transliteration-mining-system-using-finite-state-automata-framework\/","title":{"rendered":"Language independent Transliteration mining system using Finite State Automata framework"},"content":{"rendered":"
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.<\/p>\n","protected":false},"excerpt":{"rendered":"
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 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