Until recently, a majority of data annotation for training AI models was carried out manually, which invited the usual challenges that come with human intervention. Manual data annotation is prone to a variety of biases and errors and is also time-consuming.
Like humans, machines also need to learn, understand and analyze things to produce desirable outcomes. One of the most efficient ways to make machines learn is using text annotation services. With advancements in time and technology, machines have leveled up their ability to understand human language.
Data annotation also referred to as data labeling, is a process of incorporating vital information into the raw data. Initially, when the data is in an unstructured form, it is not possible for machines to comprehend. So, it is important to tag the data and make it all the more machine-friendly. Data annotation basically helps in setting up a machine learning model to make the machines understand better. The intent is to get accurate outcomes with the data available at hand.