Data labeling, also called data tagging, is the process of assigning various data points with information so that machine learning (ML) algorithms can better understand its meaning. It’s used to train machine learning models so that computing systems can output accurate information for
Data labeling, also called data tagging, is the process of assigning various data points with information so that machine learning (ML) algorithms can better understand its meaning. It’s used to train machine learning models so that computing systems can output accurate information for use in analytics and business decision-making.
Differences Between Data Labeling and Data Annotation
Data labeling and data annotation are similar but serve different purposes. Both terms are used interchangeably in some circumstances but are not the same process. Feeding a machine-learning model data is not enough for a computer to understand how to analyze and process it. Annotations and labeling describe data so that these algorithms can decipher it.
Annotations in machine learning are metadata used to describe the data. Machine learning uses large quantities of unstructured data to output meaningful information, and annotations provide every element of input information used by computing processes. For example, a picture with various elements uses annotations to define identifiable objects in the picture so that algorithms can understand and identify the same elements in future input.
Labeling is similar, but it’s used to define data types. Input into an algorithm could be text or a picture, but a computing system doesn’t know the difference between input types unless you tell it. Data labeling tags both input types so that algorithms can decipher between the two and use them to establish patterns. In a picture, it tells algorithms what type of data is present such as a human or an animal. Labeling data is critical in Natural Language Processing (NLP) to help algorithms identify aspects of human communication, including words spoken, accents, and dialects.
What Is Data Labeling Used For?
Data labeling is used in several computer applications. It’s necessary for NLP, computer vision, and speech recognition. Although it’s primarily used in these three applications, data labeling also is used in smaller proprietary applications built for corporate analytics and consumer products.
In computer vision, data labeling helps algorithms identify items within a picture. Users enter text to describe an image search, and data labeling helps algorithms identify elements of an image to return relevant results. Computer vision uses labeling and annotations to pinpoint items in images.
Tagging elements of phrases or words in NLP helps algorithms identify nuances in the way humans communicate. Labels assigned to text allow NLP algorithms to recognize special characters and use the same colloquialisms and phrases as humans with specific dialects or accents. Corporations use labels to work with spam detection, chatbots, and virtual assistance.
Speech recognition is required for products that take speech input and output it to text or perform a specific action. Transcribing applications use labels to understand video input and output text or take speech from user input on a home automation system and perform an action based on user input.
How Does Data Labeling Work?
Machine learning uses supervised or unsupervised models. Data labeling is a component of supervised machine learning, the most-used method currently. In supervised models, input is labeled and mapped to an output. Humans define labels that apply to data, so supervised models require human input.
Labeled models are fed to algorithms, and the output is reviewed. If the output is not as expected, labels would be reviewed and potentially changed to provide different input to feed algorithms again to get different output. Machine learning depends heavily on the accuracy of human-applied data labels for analysis and accurate output.
For example, a machine learning application might be asked to identify cars in thousands of pictures. Humans go through each picture and identify which ones 