Syllable Alignment: A Novel Model for Phonetic String Search

A Comparison of Personal Name Matching: Techniques and Practical Issues

“…The experimental results presented in [13] indicate that SAPS performs better than Editex, edit distance and Soundex on the same large name data set used in [25] (the COMPLETE data set we are using in our experiments as well). The authors of [13] also discuss ideas of how to adjust the fixed edit costs in SAPS by using training data to improve the matching quality.…”

“…Pattern matching techniques are commonly used in approximate string matching [15,17,22], which has widespread applications, from data linkage [5,6,28,30,31,32] and duplicate detection [2,3,9], information retrieval [13,18], correction of spelling errors [10,19,27], approximate database joins [14], to bio-and health informatics [11]. These techniques can broadly be classified into edit distance and q-gram based techniques, plus several techniques specifically developed for name matching.…”

“…• Syllable alignment distance This recently developed technique, called Syllable Alignment Pattern Searching (SAPS) [13] is based on the idea of matching two names syllable by syllable, rather than character by character. It uses the Phonix transformation (without the final numerical encoding phase) as a preprocessing step, and then applies a set of rules to find the beginning of syllables.…”

“…In [11], for example, the percentage of name mismatches in three large hospital databases ranged between 23% and 36%. To improve matching accuracy, many different techniques for approximate name matching have been developed in the last four decades [15,20,25,34], and new techniques are still being invented [13,18]. Most techniques are based on a pattern matching, phonetic encoding, or a combination of these two approaches.…”

A Comparison of String Similarity Measures for Toponym Matching

“…Syllable alignment pattern searching [15] treats syllables rather than characters as the basic unit of analysis. Syllable locations are estimated by first applying numerous context-sensitive transformations that convert characters to character groups, yielding a series of letter groups for each string [14].…”