A Comparison of Different Machine Transliteration Models
Table 16: Example transliteration ranking for data and its transliterations; WF, NWF, and RF represent WFBPS, NWFBPS, and RFBPS, respectively.
['Search Engine', '[ITALIC] e=data  [ITALIC] c1= ‘de-i-teo’', '[ITALIC] e=data  [ITALIC] c1= ‘de-i-teo’', '[ITALIC] e=data  [ITALIC] c2= ‘de-i-ta’', '[ITALIC] e=data  [ITALIC] c2= ‘de-i-ta’', '[ITALIC] e=data  [ITALIC] c3= ‘de-ta’', '[ITALIC] e=data  [ITALIC] c3= ‘de-ta’']
We used the normalized web frequency as a ranking factor. The normalized web frequency is the web frequency divided by the total web frequency of all produced transliterations corresponding to one source language word. The score for a transliteration is then calculated by summing up the normalized web frequencies of the transliteration given by the three search engines. The normalized WFBPS (NWFBPS) for search engine A was calculated as follows.