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- Introduction
- Setting up your account
- Balance
- Clusters
- Concept drift
- Coverage
- Datasets
- General fields
- Labels (predictions, confidence levels, label hierarchy, and label sentiment)
- 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
- Creating or deleting a data source in the GUI
- Uploading a CSV file into a source
- Preparing data for .CSV upload
- Creating a dataset
- Multilingual sources and datasets
- Enabling sentiment on a dataset
- Amending dataset settings
- Deleting a message
- Deleting a dataset
- Exporting a dataset
- Using Exchange integrations
- Model training and maintenance
- Understanding labels, general fields, and metadata
- Label hierarchy and best practices
- Comparing analytics and automation use cases
- Turning your objectives into labels
- Overview of the model training process
- Generative Annotation
- Dastaset status
- Model training and annotating best practice
- Training with label sentiment analysis enabled
- Training chat and calls data
- Understanding data requirements
- Train
- Introduction to Refine
- Precision and recall explained
- Precision and Recall
- How validation works
- Understanding and improving model performance
- Reasons for label 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™
- Developer
- Exchange Integration with Azure service user
- Exchange Integration with Azure Application Authentication
- Exchange Integration with Azure Application Authentication and Graph
- Fetching data for Tableau with Python
- Elasticsearch integration
- Configuring general fields
- Using general fields in your application
- Self-hosted Exchange integration
- UiPath® Automation Framework
- UiPath® Marketplace activities
- UiPath® official activities
- How machines learn to understand words: a guide to embeddings in NLP
- Prompt-based learning with Transformers
- Efficient Transformers II: knowledge distillation & fine-tuning
- Efficient Transformers I: attention mechanisms
- Deep hierarchical unsupervised intent modelling: getting value without training data
- Fixing annotating bias with Communications Mining™
- Active learning: better ML models in less time
- It's all in the numbers - assessing model performance with metrics
- Why model validation is important
- Comparing Communications Mining™ and Google AutoML for conversational data intelligence
- Licensing
- FAQs and more
Communications Mining user guide
Last updated Aug 11, 2025
We will use an insurance use-case as our example. The insurer mailbox receives emails from brokers that should be triaged
to different teams for processing. In this example the dataset has already been trained and the taxonomy looks like this:
Figure 1. Example Taxonomy
This mailbox receives Renewal, Cancellation, and Admin requests which are occasionally Urgent. Communications Mining™ has been trained to recognize each of these concepts, and Communications Mining predictions can be used to triage the emails to the correct team by creating support tickets.
To ensure that the customer is responded to quickly, we can extract some key data points that will help the downstream teams
process the request. Specifically, we want to extract the policy number, insured organization name, and broker name from the
email. We can use general field extraction to do that.
Figure 2. Configured general fields
Since the policy number format is specific to this particular insurer, we configure the general field to be trainable from scratch. On the other hand, the insured organization is a type of organization, so we configure it to be trainable based on the built-in Organization general field. Finally, we notice that brokers don't always put their name into the email, so we decide to use the broker email address (available from the comment metadata) to look up the corresponding name in an internal database, rather than extracting it as a general field.
The table below summarizes these approaches.
| CONFIGURATION | WHEN TO USE | EXAMPLES |
|---|---|---|
| Trainable general field with no base general field | Most often used for various kinds of internal IDs, or when there is no suitable base general field in Communications Mining. | Policy Number, Customer ID |
| Trainable general field with base general field | Used for customizing an existing pre-built general field in Communications Mining. | Cancellation Date (based on Date), Insured Organization (based on Organization) |
| Pre-built general fields (not trainable) | Used for general fields that should be matched exactly as defined, where training would invite mistakes. | ISIN |
| Using comment metadata instead of general fields | Used when required information is already present in structured form in the comment metadata. | Sender Address, Sender Domain |
