What is data annotation?
Data Annotation is the labeling and categorization of data for AI applications. It labels the data in various formats like text, video, or images. To train the model in supervised machine learning, data needs to be precisely annotated using the right tools and techniques so that machine can clearly understand the input patterns. With high-quality annotated data, an organization can build and improve AI implementations. It improves customer experience solutions such as product recommendations, relevant search engine results, and chatbots.
What are the different types of data annotations?
The types of data annotations are text, image, audio, and video annotation. It annotates the object in the content and makes sure it can be recognized by the machines through computer vision while ensuring accuracy.
In-text, different types of annotations are used such as – sentiment annotations, intent annotations, semantic annotations, and named entity annotations. There are different types of image annotation like bounding box annotation, polygon annotation, semantic segmentation, landmark annotation, polylines annotation, and 3D point cloud annotation.
What are the benefits of data annotations?
- Annotated data train the machine learning model and improve the accuracy of output.
- ML-based AI models help provide an enhanced and seamless user experience.