communications-mining
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- Getting started
- Balance
- Clusters
- Concept drift
- Coverage
- Datasets
- General fields (previously entities)
- Labels (predictions, confidence levels, hierarchy, etc.)
- Models
- Streams
- Model Rating
- Projects
- Precision
- Recall
- Reviewed and unreviewed messages
- Sources
- Taxonomies
- Training
- True and false positive and negative predictions
- Validation
- Messages
- Administration
- Manage sources and datasets
- Understanding the data structure and permissions
- Create a data source in the GUI
- Uploading a CSV file into a source
- Create a new dataset
- Multilingual sources and datasets
- Enabling sentiment on a dataset
- Amend a dataset's settings
- Delete messages via the UI
- Delete a dataset
- Export a dataset
- Using Exchange Integrations
- Preparing data for .CSV upload
- Model training and maintenance
- Understanding labels, general fields and metadata
- Label hierarchy and best practice
- Defining your taxonomy objectives
- Analytics vs. automation use cases
- Turning your objectives into labels
- Building your taxonomy structure
- Taxonomy design best practice
- Importing your taxonomy
- Overview of the model training process
- Generative Annotation (NEW)
- Dastaset status
- Model training and annotating best practice
- Training with label sentiment analysis enabled
- 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
Training using Search (Refine)
Communications Mining User Guide
Last updated Nov 27, 2024
Training using Search (Refine)
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 theTeach
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.
- Go to Validation.
- Select one of the recommendations.
You are redirected to the Discover page once you select a recommendation.
-
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 click the Apply labels button to bulk annotate messages:
Attention: Remember to also apply all the other relevant labels to the messages while searching, to avoid partial annotating.