Your topic or entity model might lack training data on the pattern that you are expecting.

1. Retrain the model using appropriate training data.
   • To train a topic model, see Creating machine learning topic detection models.
   • To train an entity model, see Building machine learning text extraction models.

In the case of an email bot, the email parser might not be trained properly to distinguish between the disclaimer, signature, and email body. As a result, the email parser wrongly identifies the email body as the signature or disclaimer. Typically, the text analyzer associated with the email channel is configured not to perform topic detection on the signature or disclaimer, which is why you are not getting the expected results.

1. Run a test on the email parser model, using the email text for which the topic is not detected.
   For more information, see Testing an email parser.
   Result: In the test results, you might notice that the email parser wrongly classified the email body as the signature or disclaimer. This indicates that the email parser model does not have relevant training data.
2. Retrain the email parser.
   For more information, see Training an email parser.

Your system might contain multiple versions of a text analyzer and associated models in different rulesets. The application uses the prediction and model versions in the ruleset that it can access through its own application stack. These versions might not be the versions that you updated, which is why you are not getting the expected results.

1. Ensure that the text analyzer and the models that you want to use are in a ruleset that belongs to your application stack.