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What Is Automated Data Annotation?[edit]

Automated data annotation refers to the process of labeling data with text or images according to the needs of the user. The use of such technology in education and business has increased in recent years, especially with the use of computer software for data analysis. This service is in great demand. There are many companies that provide automated data annotation services. One such company is iMerit. With such an automated system, students and business executives can take full advantage of what is otherwise a very time-consuming and labor-intensive process. Before the advent of automated data labeling systems, students had to learn how to classify their own data according to a set of rules or manual specifications. These manual specifications often had to be changed as the situation dictated, making the entire process very tedious. In addition, since many computer software programs are not compatible with all computers and operating systems, the whole process often required reinstalling the software on every machine that would be used for data labeling. Today, things have become much easier. There are now several automated data annotation tool markets available for both classroom use as well as for business use. Such a machine does not have to be installed on every desktop as before; it can simply be connected to a web server and the appropriate software downloaded. The machine should be able to read the appropriate languages used in the application (for example, a machine used in a South African classroom could read-only English text, while one used in Japan would translate everything into Japanese) and then, based on the pre-programmed language settings, proceed to assign classifications to the text and graphics it detects. Each classification of data handled by the automated data annotation market can be run separately, in parallel if necessary. The machine can also run several applications at the same time, each running in its own segment. This means that different types of data can be classified using different applications in the same application (for example, classified data may be used for segmentation and further analysis according to different specifications in order to create reports). The applications, in turn, can run on their own dedicated processors without affecting the performance of other applications. When using machine learning applications for automated data annotation, the accuracy of the classifications depends largely on the training data used in the assignments. However, even when using machine learning for labeling tasks, one still needs a good training set in place before the data sets can be labeled.