communications-mining
latest
false
- 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 or delete a data source in the GUI
- Uploading a CSV file into a source
- Preparing data for .CSV upload
- 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
- 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)
- Dastaset status
- Model training and annotating best practice
- Training with label sentiment analysis enabled
- Understanding data requirements
- 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
Communications Mining User Guide
Last updated Dec 20, 2024
Activities
The Communications Mining Activities package allows you to consume results from the Communications Mining streams. In this tutorial you can see an example of the invoice submissions process.
You are guided on how to consume a Communications Mining stream, identify invoice submission requests and download the associated attachments from the communications.
- Results - A representation of a communication that is returned from the Communications Mining stream. The results contain two key
properties:
comment- contains all the information about the communication that was uploaded to the platform, such as: the subject, body, and time stamp of the communication.prediction- contains the set of predictions that are returned against that communication. Within this property you can findextractionsandfields.
- Extraction - A prediction related to a specific instance of a request associated with a label, such as an
Address Changerequest, and the fields linked to that request: Address Line 1, Town/City, Zip code. For every label you can predict multiple extractions on each message. Each extraction has an associatedOccurrence Confidenceand anExtraction Confidence. - Field - A data point extracted as a value from a message.
A field can have the following types:
- general field (not associated with any labels).
- extraction field (linked to a specific label and required to process requests associated with that label).
- Occurrence Confidence - A model's confidence level about the presence of a specific extraction instance. For instance, how certain the model is
about a second
Change of Addressrequest in the message. - Extraction Confidence - A model's confidence level that an individual extraction is correctly extracted - i.e. the extraction is correctly identified, and all of the fields are correctly identified and associated with the correct extraction.
- Thresholding - Each label prediction returned from a stream contains a
thresholdsproperty. This property contains the list of thresholds that have been surpassed for the given prediction. Currently, the threshold that you configure on the stream is called astream.
Prerequisites
- Access to Communications Mining.
- An exchange integration configured and populating a source.
- A trained dataset based on this source.
- A stream configured on this trained dataset.
Follow the steps below, to consume
stream Results and obtain attachments.
Within a Studio project, drag in the
Get Stream Results activity from the Communications Mining activities library and select your stream.
Drag in a
For Each loop and start iterating the Results field of the variable output from the Get Stream Results activity.
Within your
For Each loop, add an If statement, and check if the Invoice Submission request has been detected with the following expression: result.Prediction.ContainsLabelExtraction("Invoice Submission")Note: You can also access any field values that you have configured for this label with the
result.Prediction.GetLabelExtractions("Invoice Submission")(0).GetField("Invoice Date") expression.
Drag in the Get Attachment activity to the
Then section of your if statement. You can then retrieve the attachment reference with the following expression: result.Comment.GetAttachmentsByType("pdf")(0).AttachmentReference.
Note: This expression assumes that at least one PDF attachment exists. Check in production that this is the case.
You can now use the downloaded attachment and pass it Document Understanding:
