We recommend following these best practices to structure your taxonomy properly and ensure high model performance:
- Objectives alignment: Make sure each label serves a specific business purpose and is aligned to your defined objectives
- Distinct: It’s important that each label is specific in what it's trying to capture and doesn’t overlap with other labels
- Specific: Avoid using broad, vague, or confused concepts as they are more likely to perform badly and less likely to provide business value. Try to split broad labels out into multiple distinct labels, if possible. It’s better to go too specific with labels initially (i.e. more levels of hierarchy) and merge them up later if needed, as opposed to having to break down very broad labels manually
- Identifiable: Ensure each label is clearly identifiable from the text of the verbatims that it’s applied to
- Parent label: Use a parent label if you expect to have a significant number of other similar concepts related to this broader topic
- Child label: Make sure that every label nested under another label is a subset of that label
- Hierarchy levels: In general, try not to add more than four levels of hierarchy as the model becomes increasingly complex to train
- Label name: Don't spend too much time thinking of the perfect label name as labels can be always renamed later
- Label description: Add label descriptions to your labels (by accessing 'Labels & Entities’ in Settings) to ensure labelling consistency, which is particularly helpful if you have several people training the model
- Uninformative: Create some non-value adding labels, e.g. thank-you emails, so you can tell the platform what is / isn’t important to analyse
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