When is it appropriate to include multiple classes for a sample text in the training data for IBM Watson Natural Language Classifier?

Including multiple classes for a sample text in the training data is appropriate when experts interpret the same text in different ways, as it supports those varying interpretations. This is particularly important in contexts where the nuances of language can lead to different meanings based on perspective, tone, or context. By training the classifier with multiple classes, you are essentially acknowledging the complexity of language and the diverse ways in which text can be understood. This creates a more robust classifier that can handle the ambiguity inherent in natural language, allowing it to better serve users by reflecting the different potential categorizations a piece of text might belong to.

In scenarios where the text is very detailed, identifying a single class might indeed lead to inaccuracies, but this is not the primary reason for including multiple classes. Similarly, while marking slight differences in similar text utterances could be a valid reason for multiple classes, it doesn’t encapsulate the broader benefit of accommodating various expert interpretations. Retaining old classes from a previous training set after retraining serves another purpose, primarily focused on preserving historical data, rather than addressing the interpretation of the current text in question.