What is Data Annotation?

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You’ve completed a hefty round of raw data collection, and now you want to feed that information into artificial intelligence (AI) machines, so they can perform human-like actions. The problem: These machines can only act according to the parameters you establish for the data set. Data annotation is the primary solution that bridges the gap between sample data and AI/machine learning.

Data annotation is a process where a human data annotator goes into a raw data set and adds categories, labels, and other contextual elements, so machines can read and act upon the information.

The annotated raw data used in AI and machine learning often consists of numerical data and alphabetical text, but data annotation can also be applied to images and audiovisual elements.

See more below about how data annotation is used in applications and some of the current and future benefits the practice offers.

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Types of data annotation

Depending on what you want your AI to accomplish and what data sources it will need, different types of annotation should be used. The most common types of annotation are text, image, audio, and video.

Text annotation

Text annotation focuses on adding labels and instructions to raw text, which enables AI to recognize and understand how typical human sentences and other textual data are structured for meaning.

There are three primary categories of text annotation that elucidate different meanings within data sets:

Sentiment: In sentiment annotation, a human annotator collects training text for AI, but first, they make note of the emotional intonation and other subjective implications behind keywords and phrases. Sentiment annotation helps AI to understand the underlying meaning of texts beyond dictionary definitions. This type of annotation is particularly useful for AI-powered moderation on social media platforms.
Intent: Intent annotation resembles sentiment annotation, but in this category, the annotator focuses on labeling the human intent, or the user’s end goal, behind different statements. Intent annotation provides insight in the realm of customer service, where AI-powered chatbots need to understand what specific results or information they should deliver to a human user.
Semantic: Buyer-seller relationships drive semantic annotation, which works to provide clearer labels on product listings, so AI can suggest, or produce in search results, exactly what customers are seeking.

Image annotation

At its most basic level, image annotation focuses on labeling images with metadata, keywords, and other descriptors that explain the image in relation to other image descriptors. Image annotation makes images accessible to users who use screen readers, and it also helps websites like stock image aggregators identify and deliver photos for user queries.

Image annotation has expanded AI capabilities over the years, now adding contextual annotations to detailed images of streets and human bodies, which provide training data for self-driving vehicles and medical diagnostic tools.

Audio annotation

Many mobile and Internet of Things (IoT) devices rely on speech recognition and other audio comprehension features, but they only learn audial meanings through the practice of audio annotation. Audio annotators handle raw data in the form of speech and other sound effects, and then they label and categorize audio clips based on qualities like pronunciation, intonation, dialect, and volume, among others. IoT devices like home assistants rely on the speech and audio recognition that comes from audio annotation.

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Video annotation

Video annotation combines several features of image and audio annotation, helping AI to assess the meaning of sound and visual elements in a video clip. Video annotation has become particularly important in the development of technologies like self-driving cars and in-home IoT devices.

Data annotation features

In every type of data annotation, a few key tools help make annotation possible:

Ontologies: Think of ontologies as the blueprints to provide accurate and helpful annotation frameworks. Ontologies include information like annotation types, labeling guidelines, and class and attribute standards.
Sample sets of smart data: You can’t practice data annotation without the right sample data. Raw data comes in endless forms, so it’s important to pick “smart” raw data or data that is relevant to the training of your specific AI tools. 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.
Data set management and storage tools: Annotating data for AI and machine learning projects requires a large amount of raw data. To keep both raw and annotated data organized and easily accessible, you need to manage and store it in a file system or software that can handle the bandwidth.

Benefits of data annotation

Data annotation impacts a wide variety of AI and machine learning technologies and brings many benefits to companies and their customers:

Chatbots and voice assistants have been trained to have more human-like conversations with customers.
Higher-quality results are returned for search queries.
In-home IoT devices can detect everything from a human voice to a sudden movement in the home, which improves accessibility and home security.
Online videos, images, and articles have become increasingly accessible for users who have vision or hearing impairments. Speech recognition technology has increased the range of accessibility on mobile and desktop devices as well.
Facial and bodily recognition tools can be used for anything from increased biosecurity to AI-powered medical diagnoses.
New technologies like self-driving cars can read and implement scenario-based data that replaces most human actions.

Data annotation use cases

With so much raw data to sort through in AI development, enterprises can rely on annotation software to simplify the process.

Companies are using the software to better understand and manage their data, which is evident in the following customer reviews:

“We got into a custom project from a client where they own a bunch of stores and they wanted us to create models based on their video analytics data to analyze the behavior of incoming customers, to have a better idea of how people are reacting to certain things that are placed near or farther from them. … I have had experience with different ML programs as well but Amazon Sagemaker stands out to be my favorite.” -Data analyst in the services industry, review of Amazon Sagemaker at Gartner Peer Insights

“Quality annotations by Playment have helped us achieve higher accuracy of our models in a very short time. Flexible solutions, QA process, and a dedicated project manager helped us have peace of mind. The team was able to experience a real off-loading of annotation needs.” -Machine learning specialist in the automotive industry, review of Playment at Playment’s website.

Data annotation market

The data annotation market, as well as the job market for data annotators, has grown with the growth of personal and corporate AI and machine learning applications.

The global annotation software market grew to around $486.1 million in 2020 and is expected to grow at an astounding compound annual rate of 26.9% between 2020 and 2027. Revenue in this market is forecasted to reach $2.57 billion in 2027, according to Grand View Research.

Data annotation software makers

If you’re interested in expanding into AI and machine learning or need additional annotation resources, several companies offer data annotation software/consulting services: