Communications Mining
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- Getting Started
- 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, entities 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)
- Understanding the status of your dataset
- Model training and labelling 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 Analytics & Monitoring
- Automations and Communications Mining
- FAQs and More
Communications Mining User Guide
Last updated Apr 18, 2024
Understanding labels, entities and metadata
Before designing your taxonomy, it’s important to understand what should be captured by labels, entities, and metadata, to meet your objectives. There should be minimal overlaps as they all complement each other.
Labels:
- Concepts, themes and intents
- E.g. ‘Change of address request’, ‘Urgent’, ‘Status update request’, etc.
- Should not be used to capture information that is present in the metadata
Entities:
- Structured data points extracted from the text
- E.g. Policy numbers, trade IDs, URLs, dates, monetary quantities, etc.
Metadata:
- Additional structured information associated with each message
- Metadata properties can be user properties (defined and added pre-upload, e.g. NPS score), email properties (captured from emails, e.g. sender, recipients, domains, etc.), and thread properties (automatically derived by the platform for threaded data like emails and chats, e.g. # of messages in thread, thread duration, etc.)
Here are some of the key distinctions and similarities between labels and entities. The two are typically used in combination for automation, but individually they serve different purposes:
The platform makes label predictions based on text of the message (for emails, this means the subject and body of the email), as well as some metadata properties. For entities, it learns from the assigned span of text, and the context of the text surrounding that span.
