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
- Delete a source
- 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
Communications Mining User Guide
Last updated Nov 19, 2024
Training using Search (Explore)
User permission required: View sources AND Review and annotate.
Even if training using
Search is not one of the main steps outlined in the Explore phase of the training, it can still be a useful training tool in any point of the training process.
Training using Search (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.
Key differences between 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.
Type your search term in the search box, at the top right of the page:
Example search query in Explore
To use
Search in the Train tab, as another step from the Explore phase of the training:
- Click the generic search recommendation in Train:
- Select the label from the search list:
- Review the LLM-powered label search suggestions.
- Add the search term and preview the results (including the number of approximate matches):
Note: The Batch has six results on a page, with typical Discover-like annotating experience (bulk + individual).
- After annotating messages on the page, click Done.
- a summary of training actions and options to
closeor search for examples for a different label, if the label doesn’t hit the criteria for no longer recommending search.
