communications-mining
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- Introduction
- 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
- Access Control and 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
- Analytics vs. automation use cases
- Turning your objectives into labels
- 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
Important :
Communications Mining is now part of UiPath IXP. Check the User Guide Introduction for more details.
Communications Mining User Guide
Last updated Mar 25, 2025
- Defining and setting up your fields
- Understanding general fields
- What pre-built templates are available for general fields?
- Standard template field types for general fields
- Enabling, disabling, updating and creating general fields
- General field filtering
- Applying advanced prediction filters
- General field Bar
- Add general field filter
- Combining general field bar filters and added general field filters
- Combining general field filters and sorting by general field for training
- Reviewing and applying general fields
- Identifying general field predictions
- How does the platform make general field predictions for trainable general fields?
- General field confidence scores
- Accepting and rejecting general field predictions
- Applying general fields
- Best Practice
- Validation for general fields
- Introduction
- How does general field validation work?
- How are the scores calculated?
- Trainable general fields
- Pre-trained general fields
- What do the summary statistics means?
- Metrics
- Understanding general field performance
- Individual general field performance
- Improving general field performance
- Overview
- General field recommended actions
- General field training modes
- Using Teach General field
- Using Check General Fields
- Using Missed General Field
- Building custom regex general fields
- What are custom Regex general fields?
- Custom Regex Template
- Type-ahead validation
- Extraction preview
- Regex
- Formatting
- Variables
- String Operations
- Functions
- Upper
- Lower
- Proper
- Pad
- Substitute
- Left
- Right
- Mid
