Providing Useful Suggestions through Did You Mean

Assist users search better with suggestions from Did You Mean, which extracts data from your content sources to recommend alternatives to misspelled queries.

The feature works for all supported languages—including Mandarin, Russian, and French—and ensures that:

  • In-house terminology, customer and employee names are recognized and never "corrected"
  • Each suggestion has at least one matching document if a search client is connected to all the content sources from which the suggestions are extracted. It never happens that a user clicks on a suggestion and is greeted with a "No Results Found" message.

Other salient features of Did You Mean include:

  • Synonym Recognition: Keywords—including jargon, abbreviations, and initialisms—set as synonyms are never tinkered with. If you set up "enzootic cycle" and "EnC" as synonyms then a search for "EnC" will find results for"enzootic cycle" and Did You Mean will not make any attempt to correct "EnC."
  • Dictionary Recognition. There are a some minor differences between the English and American orthography. Whereas an American will "plow" in four letters an Englishman will go on for another two characters to "plough". The "jailer" in America is nothing like the "gaoler" in England. SearchUnify recognizes these differences is not triggered by any of those queries. It has been designed to recognize multiple correct variants of a query.

Adding Data to Did You Mean

  1. Go to NLP Manager and then open Did You Mean .

  2. Select a Content Source, Content Type, and Content Field, and then Add.

  3. A row is inserted in the table on Add. Save your settings.

You can now click Train Dictionary to include the newly-added data into your instance's Did You Mean dictionary.

Last updatedTuesday, August 4, 2020