- 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
- Preparing data for .CSV upload
- Uploading a CSV file into a source
- 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
- Understanding data requirements
- Train
- Overview
- Reviewing label predictions
- Training using Shuffle
- Training using Teach Label (Explore)
- Training using Low confidence
- Training using Search (Explore)
- Refining and reorganizing your taxonomy
- 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
- Uploading data
- Downloading data
- Exchange Integration with Azure service user
- Exchange Integration with Azure Application Authentication
- Exchange Integration with Azure Application Authentication and Graph
- Migration Guide: Exchange Web Services (EWS) to Microsoft Graph API
- Fetching data for Tableau with Python
- Elasticsearch integration
- General field extraction
- Self-hosted Exchange integration
- UiPath® Automation Framework
- 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
Refining and reorganizing your taxonomy
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.
This section explains how you can change labels by renaming, merging, or deleting them.
Merging and deleting labels are actions that cannot be undone, so be careful when doing so. If you want to make considerable changes to your taxonomy but are concerned about the outcome, you can always fork the taxonomy first by creating a copy of the dataset and then you can revert back to the old version if you are unsatisfied with the changes.
Renaming a label
Renaming a label is a simple and reversible process. You do not need to spend excessive time trying to find the perfect name for a label when initially building your taxonomy. As long as you are capturing the idea or concept with the label, you can change the name later.
Renaming a label is also the easiest way to move labels around and add layers of hierarchy to your taxonomy. For example, check the following images from the label renaming process. This label name change means that the Room Temperature label will now be nested under the Bedroom label. The model will consider all Room Temperature labels a subset of Bedroom.
To rename a label, proceed as follows:
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Navigate to the Explore tab.
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Select the gear icon for the label you want to modify in the Labels section.
A pop-up window will appear, where you can edit the label. 3. Select Rename, and edit the name of the label. 4. Select Rename label.
Merging a label
You may need to merge one label with another for a few different reasons. It might be that you have created two labels which are very similar and one label will suffice rather than two. It could also be that you have created very specific sub-labels and there are insufficient examples at that level of detail and you want to merge a label back up into its parent.
To merge one label into another, proceed as follows:
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Navigate to the Explore tab.
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Select the gear icon for the label you want to modify in the Labels section.
A pop-up window will appear, where you can edit the label 3. Select Merge. 4. Select from dropdown list the other label that you want to merge the label into. Alternatively, enter the name of the other label. 5. Select Merge label.
Deleting a label
To delete a label, either because you created it by mistake or you no longer need it, proceed as follows:
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Navigate to the Explore tab.
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Select the gear icon for the label you want to modify in the Labels section.
A pop-up window will appear, where you can edit the label. 3. Select Delete, and then Delete label.