Data Annotation: it’s definition and role in the digital platform

Data Annotation – The inception of artificial intelligence, along with machine learning, has brought a revolutionary change in the digital sphere. From typing something on Google to getting the query cleared from the chatbot, there are tons of applications that are provided with the help of AI is making our life more comfortable.

Data Annotation
Data Annotation

The advancement of technology is doing wonders, and there is a lot to see in the coming future. The utilization of AI and machine learning has just started to be implemented, and more are yet to be explored. Now, for all sorts of applications that this technology provides, data processing plays a crucial role.

Why is data the fuel for Artificial Intelligence?

Artificial Intelligence relies heavily on data as its main function is to help people provide accurate information.  Now, gathering such a massive amount of data and processing them to provide relevant results is a big challenge even for a high-tech programmed powered with AI. Well, this is where data annotation can be really helpful.  Let us understand it in a clear way.

Data annotation is referred to as the labelling of data so that it becomes more usable and feasible to process for the machine learning program. It helps in supervised learning so that all the data that the AI can clearly understand the data that are being inputted.  Now, data can be anything used on the digital platform, including:

  • Images
  • Text in any language
  • Videos of any format
  • Audio
  • Tabular data
  • 3D models

What are the advantages of data annotation?

Data annotation is benefiting the machine learning with supervised learning so that every piece of data is being evaluated in the right form. Here are some of the significant benefits of data annotation that you should know about:

  • More accuracy in the output

The first and foremost benefit of data annotation is that it helps the machine learning and AI-powered programmes in providing a more accurate and relevant result. It helps the users in solving the problem with ease and get the right information that they were looking for. The data annotation can be helpful in various aspects, such as:

  • Search engines results
  • Relevant product recommendations
  • Showing useful results on the e-commerce platform
  • Improved customer experience

Another significant advantage of data annotation is that it results in the improvement of customer experience.  It helps the machine learning algorithm in understanding the data more clearly and ensure that the user enjoys a seamless and efficient experience. With the process of data annotation, chatbots will be able to respond clearly and faster to the query made by the user.

Also, the search engines will be able to provide better and relevant results, whether the input is given on the text format or via speech recognition.

What are the types of data annotation?

There are various types of data annotation that serve a different purpose to the AI and machine learning program.

  • Semantic annotation

It the most common type of annotation that comprehends data that are is in the text format like the name of the person, brand or any objects. With semantic annotation, machine learning languages can come up with new concepts by using the text, so it acts as the reference.

Search engine relevancy and chatbot are the two areas where the semantic annotation is performed.

  • Image and video annotation

The annotation of data helps the machine learning programs recognize and block any sensitive content that not should be shown to all.  It helps in understanding the content of the image and videos and takes it as a distinct object.

  • Entity annotation

It is the type of data annotation that labels any unstructured form of data with useful information so that it becomes easy for the machine learning to understand.

Entity annotation is being used for a multitude of the process that works in making machine learning models provide accurate output. Here are some of the major ones that you should know:

  • Named Entity recognition:  It describes the process of classification of named titles present in any test format. These can be anything based on the predefined category such as person, organization, and place.
  • Intent extraction:  These help in understanding the intent of the user, which is very much used in the chatbot features.

The use of data annotation is on the rise and will keep on increasing further because of the ever-growing demand in the tech industry. Thus, if you are thinking of becoming a data annotation specialist, then you can do a certified course.

In need for funds to pay the fees and manage expenses while doing the course, you can approach a reputed lender who provides loans for unemployed.

Spread the love

Article Author Details

Johnson Anna