- Introduction
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- General fields
- Labels (predictions, confidence levels, label hierarchy, and label sentiment)
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- Extraction Fields
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- Understanding labels, general fields, and metadata
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- Turning your objectives into labels
- Overview of the model training process
- Generative Annotation
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- 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
- Uploading data
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- Exchange Integration with Azure service user
- Exchange Integration with Azure Application Authentication
- Exchange Integration with Azure Application Authentication and Graph
- Migration Guide: Exchange Web Services (EWS) to Microsoft Graph API
- Fetching data for Tableau with Python
- Elasticsearch integration
- General field extraction
- Self-hosted Exchange integration
- UiPath® Automation Framework
- 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
Extraction fields in Communications Mining, the data points linked to specific labels and predicted by the LLM-based Generative Extraction capability.
Extraction fields are the data points associated with specific labels required to process the task associated with that label. The LLM-based Generative Extraction capability predicts them.
Before you set up extractions, it is important to understand the required data points to facilitate workflow augmentation, or end-to-end automation for the processes the labels in your taxonomy represent.
Example of extraction fields
In the following example, the platform can identify the relevant extraction fields that can facilitate the end-to-end automation of the two labels:
You can set up extractions from the following tabs:
- Train
- Explore
- Settings
For more details on how to set up extraction fields, check.