- Introduction
- Setting up your account
- Balance
- Clusters
- Concept drift
- Coverage
- Datasets
- General fields
- Labels (predictions, confidence levels, label hierarchy, and label sentiment)
- Models
- Streams
- Model Rating
- Projects
- Precision
- Recall
- Annotated and unannotated messages
- Extraction Fields
- Sources
- Taxonomies
- Training
- True and false positive and negative predictions
- Validation
- Messages
- Access Control and Administration
- Manage sources and datasets
- Understanding the data structure and permissions
- Creating or deleting a data source in the GUI
- Uploading a CSV file into a source
- Preparing data for .CSV upload
- Creating a dataset
- Multilingual sources and datasets
- Enabling sentiment on a dataset
- Amending dataset settings
- Deleting a message
- Deleting a dataset
- Exporting a dataset
- Using Exchange integrations
- Model training and maintenance
- Understanding labels, general fields, and metadata
- Label hierarchy and best practices
- Comparing analytics and automation use cases
- Turning your objectives into labels
- Overview of the model training process
- Generative Annotation
- Dastaset status
- Model training and annotating best practice
- Training with label sentiment analysis enabled
- Training chat and calls data
- Understanding data requirements
- Train
- Introduction to Refine
- Precision and recall explained
- Precision and Recall
- How validation works
- Understanding and improving model performance
- Reasons for label 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™
- Developer
- Exchange Integration with Azure service user
- Exchange Integration with Azure Application Authentication
- Exchange Integration with Azure Application Authentication and Graph
- Fetching data for Tableau with Python
- Elasticsearch integration
- Configuring general fields
- Using general fields in your application
- Self-hosted Exchange integration
- UiPath® Automation Framework
- UiPath® Marketplace activities
- UiPath® official activities
- How machines learn to understand words: a guide to embeddings in NLP
- Prompt-based learning with Transformers
- Efficient Transformers II: knowledge distillation & fine-tuning
- Efficient Transformers I: attention mechanisms
- Deep hierarchical unsupervised intent modelling: getting value without training data
- Fixing annotating bias with Communications Mining™
- Active learning: better ML models in less time
- It's all in the numbers - assessing model performance with metrics
- Why model validation is important
- Comparing Communications Mining™ and Google AutoML for conversational data intelligence
- Licensing
- FAQs and more

Communications Mining user guide
Using general fields in your application
Communications Mining™ provides multiple ways of fetching predictions, including predicted general fields. Please consult the data download overview to understand which method will work best for your use case.
Whichever method you choose, you need to be aware of the following edge-cases, and handle them in your application:
- Not all expected general fields are present in the response
- The response contains multiple matches for one or more general fields
- Not all general fields present in the response are correct
In this section we will go through each one of these edge-cases in more detail.
Note that you can use the metadata in the response when handling such cases. For example, we can choose to preferentially pick policy numbers that appear in the email subject over those that appear in the email body. The example below shows the response that the API will return for our example email.
{
"predictions": [
{
"uid": "aa05ba2250de48e3.7588b85f68f81c3b",
"labels": [...],
"entities": [
{
"id": "6a1d11118b60868e",
"name": "policy-number",
"span": {
"content_part": "body",
"message_index": 0,
"utf16_byte_start": 200,
"utf16_byte_end": 222,
"char_start": 100,
"char_end": 111
},
"kind": "policy-number",
"formatted_value": "GHI-0204963"
},
{
"id": "6a1d11118b60868e",
"name": "policy-number",
"span": {
"content_part": "subject",
"message_index": 0,
"utf16_byte_start": 0,
"utf16_byte_end": 22,
"char_start": 0,
"char_end": 11
},
"kind": "policy-number",
"formatted_value": "GHI-0068448"
},
{...},
{...},
{...}
]
}
],
"model": {
"version": 31,
"time": "2021-07-14T15:00:57.608000Z"
},
"status": "ok"
}
{
"predictions": [
{
"uid": "aa05ba2250de48e3.7588b85f68f81c3b",
"labels": [...],
"entities": [
{
"id": "6a1d11118b60868e",
"name": "policy-number",
"span": {
"content_part": "body",
"message_index": 0,
"utf16_byte_start": 200,
"utf16_byte_end": 222,
"char_start": 100,
"char_end": 111
},
"kind": "policy-number",
"formatted_value": "GHI-0204963"
},
{
"id": "6a1d11118b60868e",
"name": "policy-number",
"span": {
"content_part": "subject",
"message_index": 0,
"utf16_byte_start": 0,
"utf16_byte_end": 22,
"char_start": 0,
"char_end": 11
},
"kind": "policy-number",
"formatted_value": "GHI-0068448"
},
{...},
{...},
{...}
]
}
],
"model": {
"version": 31,
"time": "2021-07-14T15:00:57.608000Z"
},
"status": "ok"
}