Dictionaries are used to make your matching models and transformation rules smarter, efficient and more accurate. They are referenced within Classic and Machine Learning models to group or ignore similar names, keywords or abbreviations together.
Examples can include (CEO and Chief Executive Officer), people names (“Elizabeth” and “Beth” or “Charles” and “Chuck”), geographical locations (“New York, NY” and “NYC”) and so forth. Another challenge is when extra words or characters are used within abbreviations. A good example is with suffixes such as “Inc” “LLC,” “GmbH,”etc..
The Dictionaries feature is accessible through Supervisr>Dictionaries. Then create your own Dictionaries by using the Add Dictionary button or by cloning an existing dictionary and adding entries.
Entries can then be added through a bulk CSV file import or adding entries manually through the user interface.
Using Dictionaries with Matching Models
Dictionaries can be used in Machine Learning or Classic Models and assigned as either a Synonym or Ignore Words fields. See this section on how to configure your custom Matching Models
