- 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
Entity filtering
User permissions required: 'View Sources' AND 'View entities'.
Just as you can for labels, you can filter messages by whether they have entities predicted or assigned, both in Explore and Reports.
You can apply any combination of 'AND', 'OR' and 'NOT' when applying more than one entity filter. These filters can give you much greater flexibility when training and interpreting your data, and can provide much deeper insights on what's happening in your communication channels.
Here's some of the things you can now do when filtering by entity predictions:
- Apply multiple entity filters at once, in both Explore and Reports
- Filter to messages that have one of a number of selected entity predicted (i.e., Entity X OR Entity Y OR ...)
- Filter to messages that have multiple different entities predicted (i.e. entity X AND entity Y AND ...)
- Filter to messages that do not have certain entities predicted (i.e. NOT Entity Y)
- Search for entities containing specific search terms, whilst having entity filters applied
All of the entities you have enabled on your dataset will appear as shown below in the filter bar. Assigning entities is covered in detail in the next article in this section here.
There are now two ways to apply entity filters, and they can be used in combination with each other to create the right type of query.
The default state is shown above, whereby no filter is applied and all messages will be shown (unless another filter is applied).
To update the entity filter, use the buttons explained below. They change colour when selected:
| Select messages that have assigned entities | |
| Select messages that have entities predicted |
If you wish to filter to messages that have any entities assigned or predicted, use the buttons at the top (as shown above). If you want to filter to messages with specific entities assigned or predicted, hover over the entity in question and the same two buttons will appear to the right.
If you want to filter to an entity either assigned or predicted, just click the name of the entity, and it will show messages with either.
To remove your selection, simply click the button again, or to remove multiple selections, click 'All'. You can also click 'Clear All' at the top of the filter bar, but this will clear every filter you have selected, not just entity filters.
The taxonomy of entities functions as a normal filter bar, and allows you to select multiple entities at once with a single click for each.
Selecting multiple entities from the list creates an 'OR' type query.
If you selected Entity A, Entity B and Entity C in the entity bar, this creates a 'Show me messages with Entity A, Entity B, or Entity C predicted' query.
When filtering to specific entities, you can make multiple selections. For instance, you could filter to see messages that have an address line entity assigned OR a city entity predicted (as shown below).
The second filter option is the '+ Add Entity Filter' button above the entity bar.
This enables a dropdown entity bar that allows you to select more complex filters, including excluding certain entities from consideration.
From this dropdown, you can select multiple entities to include or exclude by clicking the name of the entity (for assigned and predicted), or the individual buttons (including minus for where this entity is neither assigned nor predicted).
The result would be something like the example below, which would return messages predicted to have the entity 'Address Line 1', but that also do not have the entity 'Town & City' assigned or predicted:
You can click '+ Add Entity Filter', multiple times to add additional layers to your query. Two separate entity filters create an 'AND' type query, whilst multiple entities selected in the same entity filter create an 'OR' type query.
In the example below, multiple entity filters have been applied individually. This creates a filter that will return messages predicted to have any of the three entities in the first filter, but that also have the 'Policy Number' entity predicted, and do not have the 'UK Postcode' entity predicted or assigned.
A helpful tip is that by clicking the '&' sign in an individual filter containing multiple entities, you can automatically split them out into individual filters. This would change the query from 'OR' (i.e. any of these entities predicted) to 'AND' (i.e. all of these entities predicted).
It's possible to combine filters from both the entity bar, and individually added entity filters. Filters applied in the entity bar are treated as an 'AND' query with any individually applied entity filters.
For example, in the image below, this combined query would return any messages that had the 'Address Line 1' entity predicted AND either of 'Town & City' OR 'Policy Number' predicted.
Combined entity filter using entity bar and individually added entity filters
What these new filters also mean, is that you can now apply entity filters and sort by a specific entity for a training mode.
In example below, you can see messages where 'UK Postcode' may have been applied incorrectly, but that also do not have the entity 'Town & City' predicted:
