- 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
- Overview
- Training using clusters
- Training using Search
- 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
Training using Search
- Permissions required for Automation Cloud users:
- Source - Read to view messages.
- Dataset - Read to view labels.
- Dataset - Review to apply labels.
- Permissions required for legacy users:
- View sources to view messages.
- View labels to view labels.
- Review and annotate to apply labels.
The Search functionality in the Discover page is used to search for key terms and phrases. You can search for exact search terms and if they exist it will show you these followed by partial matches. This function can be used to search for alternative terms and ways of expressing the same intent or concept for each label. This can be useful if you know a relevant common term or expression that has not appeared in any of the clusters so far and want to pin a couple of examples.
Search should not be used to apply a large number of examples per search term and per label - only a few of each.
For example, the cluster in the following image is clearly about the location of the hotel, where a Location label has been predicted. If we only used this term it could bias the model towards the phrases around the word Location or similar, and we should use the Search feature to find alternative ways of expressing this:
Possible alternative search terms for Location:
- Located
- Convenient
- Position
- Proximity
- Near
- Hotel position
- Location to transport
- Transport links
- Tourist attractions
- Close to transport
- Central
- Close to airport
- Near the airport
Searching for different terms
The following image contains an example of how searching for alternative terms for Location highlights messages that are related to the location of the hotel but expressed differently. By doing this, the model will be given different examples of Location.
Applying labels to search results
- Select Search from the Cluster dropdown menu in the Discover tab.
- Enter your search term and hit Enter or select the search icon.
- Matching search terms will appear highlighted in orange. The platform will show full matches followed by partial matches.
- Add all labels that should apply, not just your search results. For example, the Property > Staff label in the previous cluster.
You can use this process sparingly for each label that has variable ways of expressing the same topic. However, there are other methods covered in the Explore phase that also help provide different training examples, but do not have the potential to bias your model.
