What is annotation?
Annotation, also known as data labeling, is the process of annotating or labeling data, typically image data, but also videos, text, and audio. This process has become increasingly more important and popular with the rise of machine learning and supervised learning in specific. Supervised learning algorithms need to be fed training data that is labeled. Although there are a host of labeled datasets that are public and accessible, companies are seeing the importance of building their own proprietary annotated data sets. They are using data labeling software to achieve these goals.
To annotate the data, businesses can either use a third-party services provider which connects the business with labelers. Alternatively, data labeling software can be used, which provides a platform for business users to label their own data. They can also use a combination of the aforementioned methods. Some tools even provide guidance on the most effective and efficient method and will dynamically choose the source of annotation for any given data point.
Types of annotation
Data annotation can be done on a variety of data types, including images, videos, audio, and text. There are four types of annotation:
- Images: With image annotation, users can segment the images using tools such as bounding boxes, which allows them to place boxes around objects in an image. These tools can support a variety of image file types.
- Videos: Besides the tools and abilities that are part of image annotation, video annotation tools provide the ability to track unique object IDs across multiple video frames.
- Audio: Although not as common as the other types of annotation, audio annotation allows users to tag and label audio data for the purpose of speech recognition.
- Text: An emerging use case of annotation is for text data. These tools allow named entity recognition tagging (giving users the ability to extract entities from text), sentiment tagging, and more.
Key steps in the annotation process
An annotation is nothing more than a tag or a label. In order for it to be useful, it must be part of a broader data and machine learning initiative. The following are some of the key steps involved in the annotation process:
- Collecting and collating relevant data
- Determining the method and manner of annotation
- Evaluating the annotations to insurance accuracy
- Considering how these labels will be used to train algorithms
- Testing the outcome of these algorithms
- Deploying the algorithms in a production environment
Benefits of annotation
Annotation presents several distinct advantages to organizations as part of their data strategy and machine learning development. It makes it easier for machine learning engineers and other artificial intelligence practitioners to have a full understanding of their data and its labels. The following are some of the benefits of annotation:
- Improve business outcomes: Annotations are the first stage in the process of making a business more effective. Annotations help fuel supervised learning, which in turn helps improve business processes. For example, by annotating text data, a business can help train a chatbot that they can use to provide more robust and helpful customer service.
- Ensure algorithmic accuracy: By providing in-house and quality annotations, data science teams can be more confident about the accuracy of their algorithms. Although when using third-party labeling services, accuracy might be guaranteed by the provider, this is not always the case. Therefore, through annotation software, these teams can drill down into the accuracy of the labels and can create top-notch training data.
Annotation best practices
Annotations must be accurate for the algorithms to function properly. Supervised learning is fueled by labeled data. If this data is not accurate, then the outcomes and predictions will be flawed. For example, if one labels all images of cats as dogs, the system will think that a cat is a dog. The following are some best practices of annotation:
- Training: Ensure the right people are trained to use the software. This might include data scientists, as well as business users who plan to benefit from the algorithms. Proper training will save time and money in the future.
- Research service providers: Third-party providers might promise accuracy and very quick turnaround times. However, carefully consider whether or not it makes sense to use these providers, from the perspective of data security, as well as accuracy. One’s in-house team likely has more knowledge of the data, which can help ensure accuracy.
- Think end to end: Many software providers are connecting and combining annotation capabilities with broader, end-to-end training data management platforms. Annotation is only a piece of the AI puzzle.
Matthew Miller
Matthew Miller is a research and data enthusiast with a knack for understanding and conveying market trends effectively. With experience in journalism, education, and AI, he has honed his skills in various industries. Currently a Senior Research Analyst at G2, Matthew focuses on AI, automation, and analytics, providing insights and conducting research for vendors in these fields. He has a strong background in linguistics, having worked as a Hebrew and Yiddish Translator and an Expert Hebrew Linguist, and has co-founded VAICE, a non-profit voice tech consultancy firm.
