jeancsil / preDict

Lightning fast spell correction / fuzzy search library based on SymSpell by Commerce-Experts

search|hub preDict (CE) Community Edition

search|hub enables blazing fast language independent spell correction at scale

Some basics about the spell correction problem

• A closer look into the spell correction problem — Part 1
• A closer look into the spell correction problem — Part 2 — introducing preDict
• A closer look into the spell correction problem - Part 3 — the bells and whistles

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Edit Distance

preDict is based on the spell correction fuzzy search library SymSpell with a few customizations and optimization:

• The fundamental beauty of SymSpell is the Symmetric Delete spelling correction algorithm which reduces the complexity of edit candidate generation and dictionary lookup for a given edit distance. It is six orders of magnitude faster (than the standard approach with deletes + transposes + replaces + inserts) and language independent.

• Additionally only deletes are required, no transposes + replaces + inserts. Transposes + replaces + inserts of the input phrase are transformed into deletes of the dictionary term. Replaces and inserts are expensive and language dependent: e.g. Chinese has 70,000 Unicode Han characters!

preDict customizations

Our main goal was to increase accuracy whilst maintaining the increabible speed by adding:

• We replaced the Damerau-Levenshtein implementation with a weighted Damerau-Levenshtein implementation: where each operation (delete, insert, swap, replace) can have different edit weights.
• We added some customizing "hooks" that are used to rerank the top-k results (candidate list). The results are then reordered based on a combined proximity :
  • added a phonetic proximity algorithm (Eudex)
  • added a prefix proximity algorithm (Jaro-Winkler)
  • added a fragment proximity algorithm (dice coefficient)
  • added keyboard-distance to get a dynamic replacement weight (since letters close to each other are more likely to be replaced)
  • do some query normalization before search

Benchmark Results

Run on Windows10 with a Intel(R) Core(TM) i7-6700 CPU (2.60GHz) with Java(TM) 1.8.0_121

Benchmark                             Mode  Cnt   Score   Error   Units
SearchHub with PreDict EE *          thrpt  200  96,019 ± 1,007  ops/ms
PreDict CE                           thrpt  200  82,116 ± 1,149  ops/ms
Original SymSpell (Port)             thrpt  200  68,105 ± 0,977  ops/ms
Lucene (FST Based FuzzySuggester)    thrpt    5  17,588 ± 0,690  ops/ms
Lucene (Fuzzy Field-Search)          thrpt  200   0,749 ± 0,017  ops/ms

Quality Results

Based on data we collected for a few months. The test data is attached to the comparison project and can be changed. Changes to the data will, of course, change the results, but the differences shouldn't be that dramatical.

Benchmark                         Accuracy    TP   TN  Fail-Rate    FN   FP
SearchHub with PreDict EE *         98,87%  7452  355      1,13%    52   37
PreDict CE                          90,04%  6937  320      9,96%   496  307 
Original SymSpell (Port)            88,87%  6842  321     11,13%   591  306
Lucene (Fuzzy Field-Search)         88,87%  6803  360     11,13%   630  267
Lucene (FST based FuzzySuggester)   78,96%  5883  481     21,04%  1550  146

*SearchHub with PreDict EE represents our commercial offering https://www.searchhub.io This offering is a search platform independent, AI-powered search query intelligence API containing our concept of controlled precision reduction and PreDict EE (Enterprise Edition) which is capable of handling language agnostic term decomposition and disambiguation.