- 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
- Annotated and unannotated messages
- Extraction Fields
- Sources
- Taxonomies
- Training
- True and false positive and negative predictions
- Validation
- Messages
- Administration
- Manage sources and datasets
- Understanding the data structure and permissions
- Create or delete a data source in the GUI
- Uploading a CSV file into a source
- Preparing data for .CSV upload
- Create a new dataset
- Multilingual sources and datasets
- Enabling sentiment on a dataset
- Amend dataset settings
- Delete messages via the UI
- Delete a dataset
- Export a dataset
- Using Exchange Integrations
- Model training and maintenance
- Understanding labels, general fields, and metadata
- Label hierarchy and best practices
- 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)
- Dastaset status
- Model training and annotating best practice
- Training with label sentiment analysis enabled
- Understanding data requirements
- 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
Communications Mining User Guide
Extraction Fields
Extraction fields are the data points associated with specific labels required to process the task associated with that label. They are predicted by the LLM-based Generative Extraction capability.
Before you set up extractions, it is important to understand the required data points to facilitate workflow augmentation or end-to-end automation for the processes represented by the labels in your taxonomy.
In the following example, the platform can identify the relevant extraction fields that can facilitate the end-to-end automation of the two labels:
- Train
- Explore
- Settings
For more details on how to set up extraction fields, check Overview of setting up your extraction fields.
