Text

Guide for labeling text data.

Overview

When you attach a text data row to a project, the Labelbox will automatically adjust the editor interface for text labeling.

Import text data

To learn how to text data to Labelbox, visit our docs on Importing text data.

Supported annotation types

Below are all of the annotation types you may include in your ontology when you are labeling text data. Classification-type annotations can be applied at the global level and/or nested within an object-type annotation.

Import annotationsExport annotations
Entity (NER)See payloadSee payload
Annotation relationshipsComing soonSee payload
Radio classificationSee payloadSee payload
Checklist classificationSee payloadSee payload
Free-form text classificationSee payloadSee payload

Text entity

Create an entity by clicking the desired starting character and dragging to select a sequence of characters in the unstructured text.

The characters in a text file are not restricted to a single annotation. Therefore, entity annotations can overlap completely or partially.

Relationship

Create an annotation relationship with the following steps:

  1. Create two entity annotations.
  2. Use Option + Click to create a relationship between the entity annotations.
  3. Define the relationship, selecting from the existing tools in your ontology.

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Note

If desired, a relationship between two annotations can be defined with without a value. This denotes a direct relationship between the annotations without any extra modifiers.

With the annotation relationships feature, you can create and define relationships between entity annotations in unstructured text. You can then use these annotation relationships to consolidate labeling workflows and potentially reduce the number of language models needed.

Below are two common cases for using entity relationships:

Coreference resolution

Coreference resolution is the task of finding all words/expressions referencing a specific word in a body of text. This is important for NLP tasks that involve natural language understanding, such as document summarization, question answering, and information extraction.

This method usually includes relating nouns, noun phrases, proper nouns, and pronouns. For example, in the phrase, “Federer, the Swiss champion, won again because he served masterfully,” the words Federer, champion, and he are all referring to the same person and should be associated via a relationship.

Dependency parsing

Dependency parsing is the process of recognizing the grammatical structure of a sentence based on the dependencies between words.

The process establishes relationships between “head” words and their associated dependents. For example, to answer the question “Who is the opponent of Rafael Nadal in his next match?” your model would need to successfully predict the sentence structure to understand that the user wants to know the opponent of Rafael Nadal in the next match.

Text-specific hotkeys

FunctionHotkeyDescription
Create a relationshipOption + ClickSelect an entity to be the source of a relationship, then connect it to another entity to be the target.