A character-based measure of similarity is an important component of
many natural language processing systems, including approaches to
transliteration, coreference, word alignment, spelling correction, and
the identification of cognates in related vocabularies. We propose an
alignment-based discriminative framework for string similarity.
We gather features from substring pairs consistent with a
character-based alignment of the two strings. This approach achieves
exceptional performance; on nine separate cognate identification
experiments using six language pairs, we more than double the
precision of traditional orthographic measures like Longest Common
Subsequence Ratio and Dice's Coefficient. We also show strong
improvements over other recent discriminative and heuristic similarity
functions.