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According to the UiPath documentation, the Taxonomy component is used in the Document Understanding Template to define the document types and the fields that are targeted for data extraction for each document type. The Taxonomy component is the metadata that the Document Understanding framework considers in each of its steps, such as document classification and data extraction. The Taxonomy component allows you to create, edit, import, or export the taxonomy of your project, which is a collection of document types and fields that suit your specific objectives. The Taxonomy component also allows you to configure the field types, details, and validations, as well as the supported languages and categories for your documents.

In UiPath Communications Mining, the precision and recall statistics for specific labels are primarily determined by the confidence threshold set for the model. This threshold represents the level of certainty the model needs before it assigns a label to a message. By adjusting this threshold, you can trade off between precision (fewer false positives) and recall (fewer false negatives). For instance, a higher threshold will increase precision but may reduce recall, and vice versa​

Comprehensive and Detailed Explanation From Exact Extract:

Optical Character Recognition (OCR)is a method used to convert different types of documents (PDFs, scanned paper documents, images) intoeditable and searchable databy recognizing characters and their positions.

OCR is vital in Document Understanding todigitize unstructured data from images and scanned docs.

UiPath Documentation Reference:OCR Engines in Document Understanding

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In UiPath:

Variables are scoped to a specific workflow or sequence and are used to store data for operations within that scope.

Arguments, on the other hand, are used to pass data between workflows, allowing information to flow in or out of invoked workflows.

According to the UiPath documentation, the field name for a column field in Document Manager does not accept uppercase letters. It can only contain lowercase letters, numbers, underscore _ and dash -12. Therefore, the only option that meets these criteria is D. First_name123. The other options are invalid because they either contain uppercase letters, spaces, or @ symbols, which are not allowed.

According to the UiPath documentation, the recommended number of samples for the classification fields depends on the number of document types and layouts that you want to classify. The more document types and layouts you have, the more samples you need to cover the diversity of your data. However, a general guideline is to have at least 20-50 document samples from each class, as this would provide enough data for the classifiers to learn from12. A large number of samples per layout is not mandatory, as the classifiers can generalize from other layouts as well3.

In UiPath Communication Mining, the Generative Annotation feature automatically enables Assisted Labelling on datasets. Assisted Labelling helps to accelerate the labeling process by automatically suggesting relevant labels based on the content of the communications. This feature significantly improves the efficiency of the model training process by reducing the manual effort required to label large datasets.

For more details, refer to:

UiPath Communication Mining Documentation: Generative Annotation and Assisted Labelling

Labeling and Annotation in UiPath Communications Mining: UiPath AI Center Documentation