How Data Annotation Works and Its Advantages

Artificial intelligence, or AI, is one sophisticated technological innovation that brought about several advantages in both the real world, and the digital sphere. Artificial intelligence and machine learning solutions come in a wide variety of applications such as in the field of communication and research to name a few.

For instance, chat bots are now being used for businesses to ensure a 24/7 customer support. In the same manner, try typing in just a few words about any topic that you are interested in and a search engine will automatically suggest several related results.

While it is true that the development of this technology is a milestone in the industry, it can be deemed that its developers faced several challenges in order to make it seamless. One of these challenges is to overcome the need to train a significant amount of data, in order for the solution to think like a human. This is where data annotation comes in.

Data Annotation in Brief

AI needs to use machine learning models which are trained through clean and annotated data. Data annotation is a stage wherein data is preprocessed or labelled with recurring patterns in supervised learning. With supervised learning, the machine has the ability to clearly understand the desired output. Machine learning algorithms are then trained to recognize these recurring patterns or labels, even when presented with un-annotated data. 

Types of Data Annotation

Data annotation has several types and one of these is semantic annotation. There is also the image and video annotation wherein machine learning models are trained to block sensitive content or guide autonomous vehicles. Another type is the entity annotation wherein unstructured sentences are labelled with machine-readable information. Of course, there is also sentiment annotation that helps AI assess human emotions, attitudes, and opinions online.

Advantages of Data Annotation

• Output accuracy

With annotated data, the performance of AI applications and machine learning solutions are more accurate and relevant. This includes relevant product search results in search engines, as well as pertinent product recommendations on e-commerce platforms. With a machine learning algorithm trained with annotated data, only a few characters are needed for sites to be able to produce the desired results of the users.

• Enhanced user experience

With a more accurate data because of a machine learning algorithm trained with annotated data, the entire user experience is more seamless and efficient. Chat bots have the ability to immediately respond accurately to the queries of the users. In the same manner, search engines and other sites would be able to produce relevant search results, whether through a text input because of semantic annotation, or through speech recognition.

People already reaped the rewards of several technological milestones, including artificial intelligence and machine learning. AI introduced several seamless processes in different industries such as in communication, retail, or research to name a few. With more and more progress in terms of technological innovations, it can be deemed that the future is bright for various industries.

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