Performance Evaluation of Phonetic Matching Algorithms on English Words and Street Names - Comparison and Correlation

摘要

Researchers confront major problems while searching for various kinds of data in a large imprecise database, as they are not spelled correctly or in the way they were expected to be spelled. As a result, they cannot find the word they are looking for. Over the years of struggle, relying on pronunciation of words was considered to be one of the practices to solve the problem effectively. The technique used to acquire words based on sounds is known as "Phonetic Matching". Soundex is the first algorithm proposed and other algorithms like Metaphone, Caverphone, DMetaphone, Phonex etc., have been also used for information retrieval in different environments. This paper deals with the analysis and evaluation of different phonetic matching algorithms on several datasets comprising of street names of North Carolina and English dictionary words. The analysis clearly states that there is no clear best technique in general since Metaphone has the best performance for English dictionary words, while NYSIIS has better performance for datasets having street names. Though Soundex has high accuracy in correcting the misspelled words compared to other algorithms, it has lower precision due to more noise in the considered arena. The experimental results paved way for introducing some suggestions that would aid to make databases more concrete and achieve higher data quality.