Classified  s  -grams have been successfully used in cross-language information retrieval (CLIR) as an approximate string matching technique for translating out-of-vocabulary (OOV) words. For example,  s  -grams have consistently outperformed other approximate string matching techniques, like edit distance or  n  -grams. The Jaccard coefficient has traditionally been used as an  s  -gram based string proximity measure. However, other proximity measures for  s  -gram matching have not been tested. In the current study the performance of seven proximity measures for classified  s  -grams in CLIR context was evaluated using eleven language pairs. The binary proximity measures performed generally better than their non-binary counterparts, but the difference depended mainly on the padding used with  s  -grams. When no padding was used, the binary and non-binary proximity measures were nearly equal, though the performance at large deteriorated.
['Anni Järvelin', 'Antti Järvelin']
Comparison of s-gram Proximity Measures in Out-of-Vocabulary Word Translation