| Statistical Transliteration Model | We use the algorithm like calculating the edit distance between two words. |
| Statistical Transliteration Model | The edit distance calculation finds the best matching with the least operation cost to change one word to another word by using deletion/addition/insertion operations on syllables. |
| Statistical Transliteration Model | But the complexity will be too high to afford if we calculate the edit distance between a query and each word in the list. |
| Experimental Setup | The second method is to heuristically induce, where applicable, a seed lexicon using edit distance , as is done in Koehn and Knight (2002). |
| Experimental Setup | Where applicable, we compare against the EDITDIST baseline, which solves a maximum bipartite matching problem where edge weights are normalized edit distances . |
| Features | One direct way to capture orthographic similarity between word pairs is edit distance . |
| Features | Note that MCCA can learn regular orthographic correspondences between source and target words, which is something edit distance cannot capture (see table 5). |