Mixed Labeling
Mixed labeling lets you combine multiple labeling types within a single project. For example, you can label specific spans of text while also classifying the entire document, making the annotation process more flexible within one project.
Supported combinations
Span + document labeling
This allows you to label specific spans of text while also classifying the entire document. Span labeling is used to mark specific words or phrases, while document labeling provides an overall classification for the document.
When it’s useful: Use this when you need to extract key information from a document while also categorizing it based on its content.
Example use case: In contract analysis, you might highlight specific clauses related to payment terms (span labeling) while classifying the entire contract as an NDA or a Service Agreement (document classification).
Span + line labeling
Line labeling is not a standalone labeling type and cannot be created on its own. It is a sub-type of span labeling and can only be selected if span labeling is enabled during project creation.
In span labeling, you label specific words or phrases within a line. In line labeling, you label the entire line.
When it’s useful: When analyzing structured text that requires both detailed and broader classifications.
Example use case: In chatbot training, you label specific entities such as dates or product names within a sentence (span labeling) while categorizing the entire line as a customer complaint or a product inquiry (line labeling).
Span + line + document Labeling
Line labeling is not a standalone labeling type and cannot be created on its own. It is a sub-type of span labeling and can only be selected if span labeling is enabled during project creation.
This approach combines span, line, and document labeling in a single workflow. It allows you to label specific text segments (span labeling), categorize individual lines (line labeling), and classify the entire document (document labeling).
When it’s useful: When multiple levels of labeling are needed to fully understand the content.
Example use case: In medical report analysis, you label specific symptoms in a sentence (span labeling), classify the line as a diagnosis or patient history (line labeling), and categorize the document as a radiology or pathology report (document labeling).
Bounding box + document labeling
This allows you to label specific areas in an image or scanned document using bounding boxes, while also classifying the entire document. Bounding boxes mark visual elements or text regions, while document labeling assigns an overall category.
When it’s useful: When analyzing structured documents where both specific elements and overall classification matter.
Example use case: In invoice processing, you use bounding boxes to identify key fields such as invoice numbers and totals, while classifying the entire document as an invoice or a receipt.
Row + document labeling
This allows you to label individual rows in a structured dataset (such as a table or spreadsheet) while also classifying the entire document. In row labeling, you answer predefined questions about each row based on its content, while document labeling assigns an overall category to the dataset or file.
When it’s useful: When working with tabular or semi-structured data where each row is a meaningful unit, and the document also requires a high-level category.
Example use case: In transaction analysis, you answer questions about each row, such as whether the transaction is valid, a refund, or an anomaly (row labeling), while classifying the entire document as a bank statement or expense report (document labeling).
How to set up the project
Create a new project.
In step 3, when selecting the labeling type, selecting a type will display the other labeling types that can be combined.
Select all labeling types you want to use before proceeding to the next step.
Complete the remaining steps and create the project.
After the project is created, you can start labeling using all selected labeling types.
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