- 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
Discover
User permissions required: ‘View Sources’ AND ‘View Labels’.
- Firstly, it helps you to discover interesting clusters of messages. Clusters are themes of messages, which the platform has identified as sharing similar intents
or concepts.
When data is uploaded, the platform uses unsupervised learning (i.e. it reads and interprets the data without any human training) to automatically discover these clusters of similar messages and present them in the GUI. This functionality makes discovering new intents and applying labels easier and faster and is typically the first step in the model training process.
After a significant amount of training is completed, or a significant amount of data added to the dataset, Discover will retrain and present you with new clusters of unreviewed messages. When Discover retrains, it takes into account the existing taxonomy, in order to present you with new clusters that are still interesting to you.
- Secondly, Discover allows users to annotate messages in bulk, as well as individually, using either the 'Cluster' function (discussed above), or the 'Search' function. As the
messages in each cluster should contain similar intents and concepts, the bulk annotate functionality is a helpful tool to
quickly train the model.
The search function allows you to search for key terms or phrases that you know may be relevant for certain labels that you want to capture.
Discover page
