- 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
Defining your taxonomy objectives
Before you begin training your model it's important to understand how to approach your taxonomy, including creating your labels, and what they should capture. You should also define the key data points (i.e. general fields) you want to train if planning to explore and implement automation(s).
A taxonomy is a collection of all the labels applied to the messages in a dataset, structured in a hierarchical manner. It can also refer to and include the general field types enabled in a dataset, though these are organised in a flat hierarchy. This section refers to label taxonomies.
A successful use case is primarily driven by having a clearly defined set of objectives. Objectives not only ensure that everybody is working towards a common goal, but also help you decide on the type of model you want to build and shape the structure of your taxonomy. Ultimately, your objectives will dictate the concepts that you train the platform to predict.
Taxonomies can be targeted towards meeting objectives on automation, analytics, or both. When designing your taxonomy you need to ask yourself the following questions:
- To drive the automations or insights I need, what intents or concepts do I need to recognise in the data?
- Are all of these concepts recognisable just from the text of the message?
- Do certain concepts need to be structured a certain way to facilitate specific actions?
Altogether, with sufficient training, your labels should create an accurate and balanced representation of the dataset, within the context of your objectives (e.g. covering all of the request types which will be automatically routed downstream).
You may not be able to meet all your objectives with a single taxonomy in a dataset. If you want to get broad yet detailed analytics for a communication channel, but also automate a select number of inbound request types into workflow queues, you may need more than one dataset to facilitate this.
It’s usually best not to try and achieve absolutely everything at once within one sprawling multi-purpose taxonomy, as this can become very difficult to train and maintain high performance with. It's easiest to start with a taxonomy for a specific purpose, e.g. analysing in-app customer feedback data for product feature requests and bugs, or monitoring client service quality in an operations team inbox.
A breakdown of the different kinds of objectives is covered in the next article on analytics versus automation focused use cases.
