ixp
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- 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
- 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
Important :
Communications Mining is now part of UiPath IXP. Check the Introduction in the Overview Guide for more details.
Communications Mining user guide
Last updated Aug 1, 2025
Defining taxonomy
To ensure the model returns the most relevant predictions, you must define your taxonomy correctly for each dataset. This is a critical step in the generative extraction process, because the learning and predictions of the model are based on the details you define beforehand.
First, define your taxonomy as follows:
- Log in to Communications Mining™ to land on the datasets page.
- Select New dataset to create a new dataset, and fill in the required fields. For more details, check Create a new dataset.
- Select Next.
- In the Define label step, select Add label.
- Define and structure the label correctly, and provide a clear description of what it should consist of.
For example, you could add individual labels, without a structure, such as Label 1, Label 2, or you could create a hierarchy with the structure
Parent label > Branch label > Child label. This label hierarchy represents the taxonomy for this particular dataset. For more details, check Label hierarchy. - Select Create.
Note: To view or edit the taxonomy of a dataset, in the sidepanel of the Explore or Reports tabs, scroll down until you arrive to the Labels section.
Once you set up your taxonomy, determine the processes you want to automate.
Next, identify the specific data points or fields that must be extracted from the documents.
Finally, organize the data points into a coherent structure (i.e. extraction schema), which will guide the data extraction process. To build the extraction schema, set up the fields through the Train, Explore, or Settings tabs, and set up the field types.
For more details, check the following resources:
