- 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)
- Understanding the status of your dataset
- 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
Best practices and considerations
The highlighted general field should cover the entire word (or several) in question, not just part of it. Don't include additional spaces at the end of the field.
Similar to labels, don't partially review your general and extraction fields.
- General fields are reviewed at the paragraph level, not the entire message level. When you review a paragraph for fields, review all the
fields in the paragraph.
Not confirming a field in a paragraph where you have labelled other fields, tells the model that you don't consider it a genuine example of the predicted field. This is reflected in the validation scores and the general field performance.
- Extraction fields are reviewed at the message level, not just the paragraph level. When you review an entire message for fields, review all
the fields in the message.
Not confirming a field in a message where you have labelled other fields, tells the model that you don’t consider it a genuine example of the predicted field. This is reflected in the validation scores and extraction field performance.
- Global fields cannot overlap with each other, or with another example of itself.
- Global fields and extraction fields can overlap with each other.
- You can use the same span of text as many times as needed by different extraction fields.
- There is currently no general field normalization preview in Communications Mining. Fields that should be normalized will get normalized in the downstream response. Normalization in Communications Mining will be available in the model in the future.
- If a child label has extractions on it, its parent doesn’t inherit the extraction examples automatically. For labels, its parent automatically inherits the extraction examples.
- Providing additional extraction examples does not improve the performance of a label. To improve the performance of a label, focus on label-specific training.
- Improving label performance allows you to increase the likelihood that you capture occurrences where a label (and subsequently its extractions) should have been predicted.
To improve the performance of your extractions, provide validated examples on the extractions itself.
