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It produces better results than Soundex because it takes special care to handle phonemes that occur in European and Hispanic surnames. All of them process phonemes differently in an attempt to improve accuracy. Algorithms developed after Soundex use different encoding schemes, either building on Soundex by tweaking the lookup table or starting from scratch with their own rules. These types of failures illustrate a major shortcoming of Soundex. The last two names, Jessica and Joshuaare not related at all but are given the same hash value because the letters JSand C all map to the digit 2, and the algorithm removes duplicates. In this example, the variations Theresa and Teresa both produce the same Soundex hash, but Catherine and Katherine start with a different letter even though they sound the same, the hash outputs are different. The Fuzzy library includes a Soundex implementation for Python programs. A Soundex hash value is calculated by using the first letter of the name and converting the consonants in the rest of the name to digits by using a simple lookup table. Census and is specifically designed to encode names. The Soundex algorithm appears frequently in genealogical contexts because it's associated with the U. One such algorithm is Soundexdeveloped by Margaret K.
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These phonetic hash algorithms allow you to compare two words or names based on how they sound, rather than the precise spelling. Subscribe to RSSĪ better solution is to compute hash values for entries in the database in advance, and several special hash algorithms have been created for this purpose. Using a traditional fuzzy match algorithm to compute the closeness of two arbitrary strings is expensive, though, and it isn't appropriate for searching large data sets. A common way to solve the string-search problem is to look for values that are "close" to the same as the search target. These sorts of problems are especially prevalent in transcriptions of handwritten historical records used by historians, genealogists, and other researchers.
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Depending on the source and age of the data, you may not be able to count on the spelling of the name being correct, or even the same name being spelled the same way when it appears more than once.ĭiscrepancies between stored data and search terms may be introduced due to personal choice or cultural differences in spellings, homophonestranscription errors, illiteracy, or simply lack of standardized spellings during some time periods. Searching for a person's name in a database is a unique challenge.