- 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)
- 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
General fields (previously entities)
Entities as they were previously known are now General fields, one of two kinds of fields in Communications Mining™.
General fields are not associated to a certain label, while extraction fields are. These extraction fields are predicted based on their linked labels.
General Fields are additional elements of structured data which can be extracted from within the messages. General Fields include data such as monetary quantities, dates, currency codes, organisations, people, email addresses, URLs, as well as many other industry specific categories.
The below screenshot shows a message containing three predicted general fields; two monetary quantities and a policy number:
An example email message sent to an insurance underwriting mailbox containing structured data general fields: two monetary quantities and a policy number
Much like labels, predicted general fields can be accepted, rejected, or assigned by highlighting a string of text and choosing the correct general field from the list in the modal (see here for how). Both of these actions will provide training signals to the general field extraction model, which will improve its understanding of that general field type.
Enabling general field extraction and selecting the general fields to extract are confirmed either during the creation of the dataset or via the settings section in the Dataset settings page.
