Rosette text analytics uses linguistic analysis, statistical modeling, and machine learning to accurately process unstructured text and names, revealing valuable information and actionable data. Many of the calls return scores measuring confidence, salience, or matching along with the data. Each endpoint and SDK may have a unique definition and calculation for these scores.
Threshold values may be used with these scores to determine which values to return as part of the output when analyzing text documents. This article attempts to explain the meanings behind these values by function, along with some best practices for using them.
In general, your results will depend on your data. No single value will represent relevance or accuracy across all possible applications. By testing with and evaluating your own data, you can determine what values work for your environment.