- Getting started
- Balance
- Clusters
- Concept drift
- Coverage
- Datasets
- General fields (previously entities)
- Labels (predictions, confidence levels, hierarchy, etc.)
- Models
- Streams
- Model Rating
- Projects
- Precision
- Recall
- Reviewed and unreviewed messages
- Sources
- Taxonomies
- Training
- True and false positive and negative predictions
- Validation
- Messages
- Administration
- Manage sources and datasets
- Understanding the data structure and permissions
- Create a data source in the GUI
- Uploading a CSV file into a source
- Create a new dataset
- Multilingual sources and datasets
- Enabling sentiment on a dataset
- Amend a dataset's settings
- Delete messages via the UI
- Delete a dataset
- Delete a source
- Export a dataset
- Using Exchange Integrations
- Preparing data for .CSV upload
- Model training and maintenance
- Understanding labels, general fields and metadata
- Label hierarchy and best practice
- Defining your taxonomy objectives
- Analytics vs. automation use cases
- Turning your objectives into labels
- Building your taxonomy structure
- Taxonomy design best practice
- Importing your taxonomy
- Overview of the model training process
- Generative Annotation (NEW)
- Understanding the status of your dataset
- Model training and annotating best practice
- Training with label sentiment analysis enabled
- Train
- Introduction to Refine
- Precision and recall explained
- Precision and recall
- How does Validation work?
- Understanding and improving model performance
- Why might a label have low average precision?
- Training using Check label and Missed label
- Training using Teach label (Refine)
- Training using Search (Refine)
- Understanding and increasing coverage
- Improving Balance and using Rebalance
- When to stop training your model
- Using general fields
- Generative extraction
- Using analytics and monitoring
- Automations and Communications Mining
- Licensing information
- FAQs and more
Building your taxonomy structure
One of the most fundamental factors that will determine how well your model performs, as well as how well it meets your business objectives, is the structure of your taxonomy, including what is captured by each of the labels within it.
It’s therefore important to think about your target taxonomy structure in advance of model training. Having said that, you should have a degree of flexibility to adapt, expand and enhance it (as necessary) as you progress through the training. This is what we call the ‘data-led’ training approach.
Ultimately, the labels in the taxonomy, and the training examples provided for each label, should create an accurate and balanced representation of the dataset as a whole. But every label should also be valuable, by clearly representing in some way the messages for which it is predicted.
If labels are used to capture very broad, vague, or confused concepts, not only are they more likely to perform badly, they are less likely to provide business value. This can be to provide useful insights on that concept, or to help a process be fully or partially automated downstream.
This is an example of a high-level taxonomy with typical labels applicable across various use cases / industries. Not all of them will be applicable to your model.
A company receives millions of emails each year into different inboxes from clients about a multitude of issues, queries, suggestions, complaints, etc.
This company decides to increase their operational efficiency, process standardisation, and visibility as to what's going on in their business by automatically turning these emails from customers into workflow tickets. These can be then be tracked and actioned, using specified processes and within set timelines.
To do this, they decide to use the platform to interpret these inbound, unstructured communications and provide a classification regarding the process and sub-process that the email relates to. This classification is used to update the workflow ticket that will be automatically created using an automation service, and ensure that it's routed to the correct team or individual.
To make sure that this use case is as successful as possible, and to minimise the number of exceptions (wrong classifications, or emails the platform is not able to confidently classify), every inbound email should receive a confident prediction that has a parent label and a child label, i.e. [Process X] > [Sub-Process Y].
Given that the objective is to classify every inbound email with a [Process] and [Sub-process], every label in the taxonomy should conform to this format:
In this use case, any email that does not have a confident prediction for both a parent and child label could be an exception, sent for manual review and ticket creation. Alternatively, if it has a high confidence parent label prediction, but not a confident child label prediction, this could still be used to partially route the email or create a ticket, with some additional manual work to add the relevant sub-process.
If we imagine the former is true, and every email without a high confidence prediction in the form of [Process] > [Sub-Process] becomes a manual exception, we want to make sure that all of the examples we provide for each label when training the model reflect this format.
Each parent label in the taxonomy should relate to a broad process relevant to the content in the emails, e.g. 'Invoicing'. Each child label should then be a more specific sub-process that sits under a parent label, e.g. 'Invoicing > Status Request'.
Remember, it's important that each label is specific in what it's trying to capture. Extremely broad labels such as 'General Query', or 'Everything else' can be very unhelpful if used to group together lots of different distinct topics and there's no clear pattern or commonality between the pinned examples.
In this use case, they would also not provide much business value when a workflow ticket was created and classified as 'General Query' or 'Everything else'. Someone would still need to read it carefully to understand what it was about and whether it was relevant for their team before it could be actioned.
This negates any time saving benefit and would not provide useful MI to the business on what work was actually being done by the teams.
Please Note: This is just one example of how you might structure a taxonomy for a specific use case, this is not a one-size-fits-all approach. Every project will require a unique taxonomy of labels, which is highly dependent on your specific use case, dataset, and objectives.
