fuzzy-search

I'm using fuzzy matching in my project mainly to find misspellings and different spellings of the same names. I need to exactly understand how the fuzzy matching of elastic search works and how it uses the 2 parameters mentioned in the title. As I understand the min_similarity is a percent by which the queried string matches the string in the database. I couldn't find an exact description of how this value is calculated. The max_expansions as I understand is the Levenshtein distance by which a search should be executed. If this actually was Levenshtein distance it would have been the ideal

I have two sets of data. Existing customers and potential customers. My main objective is to figure out if any of the potential customers are already existing customers. However, the naming conventions of customers across data sets are inconsistent. EXISTING CUSTOMERS Customer / ID Ed's Barbershop / 1002 GroceryTown / 1003 Candy Place / 1004 Handy Man / 1005 POTENTIAL CUSTOMERS Customer Eds Barbershop Grocery Town Candy Place Handee Man Beauty Salon The Apple Farm Igloo Ice Cream Ride-a-Long Bikes I would like to write some type of select statement like below to reach my objective: SELECT a

how do you get the matching fuzzy term and its offset when using Lucene Fuzzy Search? IndexSearcher mem = ....(some standard code) QueryParser parser = new QueryParser(Version.LUCENE_30, CONTENT_FIELD, analyzer); TopDocs topDocs = mem.search(parser.parse("wuzzy~"), 1); // the ~ triggers the fuzzy search as per "Lucene In Action" The fuzzy search works fine. If a document contains the term "fuzzy" or "luzzy", it is matched. How do I get which term matched and what are their offsets? I have made sure that all CONTENT_FIELDs are added with termVectorStored with positions and offsets . user193116

I am wondering if there has any Python library can conduct fuzzy text search. For example: I have three keywords "letter" , "stamp" , and "mail" . I would like to have a function to check if those three words are within the same paragraph (or certain distances, one page). In addition, those words have to maintain the same order. It is fine that other words appear between those three words. I have tried fuzzywuzzy which did not solve my problem. Another library Whoosh looks powerful, but I did not find the proper function... {1} You can do this in Whoosh 2.7 . It has fuzzy search by adding the

I have a dictionary of 50K to 100K strings (can be up to 50+ characters) and I am trying to find whether a given string is in the dictionary with some "edit" distance tolerance. (Levenshtein for example). I am fine pre-computing any type of data structure before doing the search. My goal to run thousands of strings against that dictionary as fast as possible and returns the closest neighbor. I would be fine just getting a boolean that say whether a given is in the dictionary or not if there was a significantly faster algorithm to do so For this, I first tried to compute all the Levenshtein

I have a site that needs to search thru about 20-30k records, which are mostly movie and TV show names. The site runs php/mysql with memcache. Im looking to replace the FULLTEXT with soundex() searching that I currently have, which works... kind of, but isn't very good in many situations. Are there any decent search scripts out there that are simple to implement, and will provide a decent searching capability (of 3 columns in a table). ewemli's answer is in the right direction but you should be combining FULLTEXT and soundex mapping, not replacing the fulltext, otherwise your LIKE queries are