Resolve false negatives

• How does classification work?
• Dashboard
  • Create a model
  • Add an example model
• Models
  • Tokenization
  • Settings
  • Build/Test a model
  • Import/Export a model
  • Using a model in your application
• Categories
  • Term rules (rule-based models)
  • Term definition & syntax
  • Combine training text and rules
• Fine tuning
  • Resolve false negatives
  • Resolve false positives
• Changelog

There are two possible ways to resolve a false negative, depending on the type of model we are working with and on the scenario we have:

Using rules

There are four things you can check:

• Review the excluding terms of the category in which we want the text to be classified and ensure that none of them appears in the text excluding the category from the classification results.
• If there are mandatory terms defined, check if any of them appears in the text: if none appears, that's where the false negative comes from. To resolve it, you need to add to the list of positive terms a term that appears in the text.
• Review the irrelevant terms in case any of them are decreasing the relevance of the category. If this is the case, you should evaluate if the term in question is necessary. If it isn't, it can be deleted from the list. Nevertheless, the best way to go would be to limit the context in which the irrelevant term applies in order to exclude the case that's giving the false negative.
• Add to the category relevant terms, or if necessary, mandatory terms to increase the relevance assigned to the category.

This option can be applied to hybrid models and to rule-based one.

Using training texts

The way of resolving a false negative using training text is just to add the text that's given the false negative to the training text of the category in which it should be classified.

This option can be applied to hybrid models and to statistical ones.