Data annotation involves processing a set of raw data for text, images, sounds, and videos to be used in AI and ML projects.
In this definition...
What is data annotation?
Data annotation is a process where a human data annotator adds categories, labels, and other contextual elements to a set of raw data. Machines can then read and act upon the information based on the criteria that were set.
How is data annotation used?
Data annotation is the primary solution that bridges the gap between sample data and artificial intelligence/machine learning (AI/ML). It can be used for the numerical and alphabetical data in AI/ML as well as images and audiovisual elements.
Portions of this definition originally appeared on Datamation.com and are excerpted here with permission.
What are the types of data annotation?
The four most common types of data annotation are text, image, audio, and video, which can be used according to AI needs and data sources.
Text annotation allows AI to recognize and understand the meaning of typical human sentences and other textual data by adding labels and instructions to raw text.
There are three primary categories of text annotation:
- Sentiment: Sentiment annotation makes use of training AI to understand underlying meaning of texts beyond dictionary definitions by making note of emotional intonation and subjective implications. This can be useful for AI-moderated social media platforms.
- Intent: Similar to sentiment annotation, intent annotation focuses on labeling the human intent, or the user’s end goal, which can be useful for AI-powered chatbots that need to understand what specific results or information they should deliver to a human user.
- Semantic: Semantic annotations are great for building buyer-seller relationships. It works by providing clearer labels on product listings, so AI can suggest or produce in search results exactly what customers are seeking.
Image annotation involves labeling images with metadata, keywords, and other features that explain the image in relation to other image descriptors. This helps make images accessible to those who use screen readers, and it also helps websites like stock image aggregators identify and deliver photos that meet users’ search criteria. And as AI capabilities have expanded, image annotation has become useful in providing training data for self-driving cars and medical diagnostic tools.
Many mobile and Internet of Things (IoT) devices, such as home assistants, that have speech recognition and other audio comprehension features rely on audio annotation. Audio annotators take raw data in the form of speech and other sound effects and label and categorize it based on qualities like pronunciation, intonation, dialect, and volume among others.
Video annotation helps AI assess the meaning of sound and visual elements in a video clip through a method of annotation that combines several features of image and audio annotation. Some examples of video annotation are used in the development of self-driving cars and in-home IoT devices.
Go deeper on data annotation including more use cases at Datamation.com.
What are data annotation’s features?
In every type of data annotation, a few key tools help make annotation possible:
- Ontologies: As the blueprints for accurate and helpful annotation frameworks, ontologies include information like annotation types, labeling guidelines, and class and attribute standards.
- Sample sets of smart data: When training specific AI tools, it’s important to pick smart, or relevant, raw data. This data is usually collected from historic human interaction data the company has on file, but sometimes, open-source data will meet the needs of the data annotation project.
- Dataset management and storage tools: AI and ML projects often require large amounts of raw data to be annotated, so to keep both raw and annotated data organized and easily accessible, users need to manage and store it in a file system or software that can handle the bandwidth.