ixp
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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
Use the Search action as part of the model training process, for labels that are less frequently occurring and don’t regularly appear in clusters and/or shuffle mode.
If there are minimal initial training examples for a label, you can use Search sparingly for one or more terms, for a given label. This provides sufficient examples for the Teach action to be available, for example, by displaying roughly half of the examples relevant for that label.
Note: Using the Search action too much can
lead to annotating bias, and over-fitting the model’s understanding of a label concept
into specific terms/phrases, instead of understanding the broader context and
variability of the concept itself. This means that you can overuse search unless
guardrails are provided by the platform.
To use the Search recommendation in the Validation page, apply the following steps:
- Navigate to the Validation
tab.
- Select one of the
recommendations, which redirects you to the Discover page.
- Search for terms or expressions
related to the label you are searching for.
Note: Apply examples sparingly using search for any label to avoid annotating bias. - Add a label, then select Apply
labels to bulk annotate messages:
Attention: Remember to also apply all the other relevant labels to the messages while searching, to avoid partial annotating.
Note: To better understand how to use Search
in the Explore tab, check Training using Search
(Explore).
