If you’ve been hanging around the Remotasks chat channels for a while, chances are a lot of fellow Remotaskers have suggested that beginners should do annotation Tasks first. However, it’s perfectly understandable to feel a bit lost whenever people throw that term around in the same way we commonly say “egg sandwich” or “hamburgers.” 

What exactly does data annotation mean? Is it really just annotating data, like what it literally states? Well, it’s a bit of a yes and no. 

First Things First: What Is Data Annotation?

At its core, Data Annotation is the process of labeling the contents of a material that can be recognizable by computer vision, or natural language processing (NLP). When we label or annotate these kinds of data, they become much easier to feed into algorithms or programming to be interpreted via NLP. 

It’s thanks to data annotation that artificial intelligence (AI) or a machine learning model can interpret data in high quality images and videos, as well as text. Data annotation makes it possible for machine learning projects such as autonomous vehicles to successfully bring us to our destinations. 

Data Annotation And AI: What’s It For?

Since AI can interpret vast amounts and types of data, a machine learning model can make mistakes when encountering new information. Data labelers help AI conduct data annotation properly through supervised machine learning. This is a process that allows labelers such as Remotaskers to train AI to properly conduct data annotation on various kinds of material.

Additionally, this human in the loop scenario lets us help dictate and correct output whenever a machine learning algorithm or an AI model commits a mistake in data interpretation.

Here are some of the most popular types of data annotation, and why it can be a meaningful process for machine learning:

• Semantic Annotation: Semantic annotation or text annotation pertains to the process of data labelers training AI and machine learning to identify relevant aspects of a user’s interaction with tools such as chatbots and virtual assistants. Thanks to metadata and keywords, NLP can make use of text annotation to give accurate responses depending on the text cues of the user. 
• Speech Recognition: When data annotation is done to text, it’s simply a way of helping AI and machines improve their speech recognition. Through annotation, AI can better understand the communication and speaking process between humans, especially when they use their native languages. In terms of practical use, AI can make use of text annotation to fully understand what their users say and provide meaningful responses. Sometimes, text annotations also make use of metadata to help identify keywords more accurately to give more useful answers.
• Image Annotation: Image annotation is perhaps the most important aspect of data annotation. AI and machine learning can make use of various identification processes to annotate images and attach specific meaning to them. These special processes utilize special techniques to create unique data sets to train AI. These include 3D point annotation, polygon annotation, landmark annotation, semantic segmentation, and bounding boxes to separate elements in an image. Users commonly need to use a bounding box to label and identify different objects in a visual media, in this case an image.
• Video Annotation: Unlike text annotation, video annotation makes full use of video to interpret what’s happening amongst multiple moving objects. Through video annotation, objects are analyzed on a frame by frame basis. Autonomous vehicles such as self-driving cars can use training data with video annotation to help identify and avoid obstacles. 

Annotation And Remotasks

If you’re interested in experiencing data annotation for the first time, then you might want to join our team in Remotasks! Data Annotation is one of the most popular project types in Remotasks, and it’s certainly a favorite among our Remotaskers. You can just go to our website, sign up for free, and get onboarded to do Data Annotation tasks in less than an hour! What’s better is that we actually pay you to do Data Annotation tasks for us!