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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
- 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
Note: You must have assigned the Source - Read
and Dataset - Review permissions as an Automation Cloud user,
or the View sources and Review and annotate
permissions as a legacy user.
Even though training through Search is not a primary step in the Explore phase, it can still serve as a useful training tool at any stage of the training process.
Training using Search in the Discover page describes how to use the search action sparingly. Avoid using it too much, as it can bias your model.
Search for terms or phrases in Explore in the same way as in Discover.
Comparing using Search in Explore and Discover
- In Explore you must review and annotate search results individually, rather than in bulk, like Discover.
- Explore provides a helpful approximation of the number of messages that match your search terms. Check the following example for cancellation searching.
Search for a few relevant terms or phrases, and check how many approximate matches are in the dataset. Use this to estimate whether you have enough examples for a certain label.
Enter your search term in the search box as shown in the following image:
To use Search in the Train tab, as another step from the Explore phase of
the training, proceed as follows:
- Select the generic search recommendation in the Train tab:
- Select the label from the search
dropdown list:
- Review the LLM-powered label search suggestions.
- Enter the search term and preview the results, including the number of approximate matches.
- Select Train these messages or search for another term before you
proceed.
Note: The Batch has 6 results on a page, with annotating experience similar to Discover, bulk and individual.
- Once you have annotated the messages, select Done. This action displays a pop-up window informing you that the training batch is complete. This window also includes a summary of training actions and options to close or search for examples for a different label, if the label does not meet the criteria for no longer recommending search.
