Data labeling is the process of identifying and labeling samples of data used in Machine Learning. Labeling is especially critical when it comes to supervised learning, where the input and output data are classified into categories to facilitate an AI model’s future learning. Data tagging, annotation, moderation, categorization, etc., are just some of the components of an information labeling workflow. Some common types of data labeling are:
1. In-house data labeling
The best quality labeling is achieved through in-house data labeling, often performed by data engineers and scientists employed by the company. It is of great importance in the insurance or healthcare industries, as accurate labeling plays a significant role.
2. Crowdsourcing
Crowdsourcing utilizes a huge group of freelancers who have signed up for a crowdsourcing platform to collect annotated data.
3. Outsourcing
Data annotation can be outsourced to a company or a person, creating a compromise between in-house data labeling and crowdsourcing. Individuals can be evaluated on their knowledge of the subject before the work is given, which is a major benefit of outsourcing.
Applications of Data Labeling
Labeling data is an essential part of developing ML models. In order to aid an AI model in learning and making reliable predictions, it is labeled manually with meaningful tags.
1. Computer Vision
Labeled data is a crucial component of computer vision research, which aims to teach computers to “see” their surroundings. In computer vision, data labeling is done to add relevant information as tags or annotations to raw data. In compute vision, data labels are created as digital outlines around specific objects in an image. This helps the computer understand the different portions of the image for classification, which in turn forms the basis for data processing by the ML models.
2. Natural Language Processing
Data labeling is used to train NLP models to predict the different attributes that enable the algorithm to understand spoken or written language. Data labeling in NLP can be in the form of labeling utterances, the intent, and entities that represent real-time objects such as people, organizations, locations, values, etc.
3. Audio Annotation
In audio annotation, labels are used to distinguish the different sounds in an audio dataset and tag them with specific keywords. This type of annotation is quite critical during the development of AI-assisted applications such as chatbots, virtual assistants, voice recognition systems, etc.
