Data Annotation

Types of data annotation

Data annotation is a broad practice that encompasses different types of data, including image, text, audio and video. Each type of data has its own unique challenges when it comes to annotation.

Image Annotation for Computer Vision

Image annotation involves creating bounding boxes (for object detection) and segmentation masks (for semantic and instance segmentation) to differentiate the objects of different classes. Image annotation is often used to create training machine learning datasets for the learning algorithms

Text Annotation

This kind of annotation is the addition of relevant information about the language data by adding labels or metadata.
Learn more about text annotation for Machine Learning

Audio Annotation

Audio annotation is the process of recording and transcribing speech, with a focus on phonetics, accents, and speaker demographics. Every use case is different; some require a very specific approach such as tagging aggressive speech indicators for emergency hotline technology applications. The term “data annotation” can refer to anything from annotating the content of an audio file to annotating a single word. Several factors affect how efficient a system is for processing information, and data annotation helps with this process by identifying them all. Non-verbal cues such as silence or background noise are also annotated in order to make algorithms more efficient.

Video Annotation

Video annotation is the task of labeling sections or clips to be used to identify, classify, or detect the desired objects in a virtual environment. This is done using the same techniques as image annotation like bounding boxes or semantic segmentation, but on a frame-by-frame basis. Annotation is an essential technique for computer vision tasks such as localization and object tracking. By annotating videos, we can provide valuable information that can be used to improve these tasks.

Semantic Annotation

Semantic annotation refers to adding tags to various concepts, such as people, organization names, places, etc., in the document. This will assist ML models in dividing the new concepts in the future text into appropriate categories.

This annotation is essential for AI and ML training to enhance Chabot’s capabilities and improve search relevance. The semantic annotation usually includes tagging key phrases and the correct identification parameter.

What Things do You Need to Keep in Mind for Data Annotation in Machine Learning?

Now that you have a clear idea about data annotation and why it’s necessary for your ML projects, the next important thing is using it properly. If you want to make the most out of the data annotation, you need to see it as a part of your ML workflow.

It will require you to come up with a mixture of software elements, algorithms, annotators, etc. Also, you need to ask two questions for your data annotation project:

• How are you going to utilize the limited resources for data annotation effectively?
• How will you assess the quality of your annotations?

You can use various techniques to address these problems. To give you a better idea, we’ll give an overview of the two most effective techniques, which are :

• Active learning
• Quality assessment

Active learning tells you the methods to sample the data for annotation, whereas quality assessment is about validating annotation performance. Let’s discuss them in more detail.

Active Learning: Ways to Sample Data for Annotation

Active Learning refers to choosing data samples while keeping the point of data annotation at the forefront.

Since the resources necessary for the data annotation are scarce, you must utilize them effectively. You can choose from different types of active learning for data annotation. It can help you save time and money. Below are the three most common ones that most people use.

Diversity Sampling

Diversity sampling refers to the general paradigm that attempts to discover underrepresented or unknown values in your model. It can come in handy when you have to choose from various options. It is also known as:

• Stratified Sampling
• Representative Sampling
• Anomaly or Outlier Detection

One of the key advantages of using this tool is that it can allow the model to learn about underrepresented information and details. In some cases, ML models will ignore certain information in the datasets due to their low occurrences. However, diversity sampling allows them to learn about such models.

Additionally, diversity sampling helps you avoid performance loss because of data drift. It usually happens when the AI or ML model contains too much data from the old and inaccurate sample regions.

Uncertainty Sampling

Uncertainty sampling refers to the process of choosing unlabeled samples that are close to the decision-making capabilities of the model. The benefit of this method is that you can identify samples with a higher possibility of being wrongly classified. So, you can manually annotate them to mitigate the chances of any errors.

Random Sampling

Lastly, random sampling is also a kind of active learning, and it is the simple one you can use. But the one challenge you might face is finding a random sample might be easy due to the distribution of the received data. Also, there are specific issues that you can catch with other methods but not with random sampling.

Quality Assessment: Validating Annotation Performance

Once you complete the sampling step, the next thing to do so is a proper QA. There is a chance the annotators can make mistakes or fail to identify any possible drawbacks. Therefore, introducing proper checkpoints to spot these mistakes is vital. Below we have covered some points to help you enhance the annotation performance:

• Have Annotators with the Right Expertise: Having experienced annotators and experts can offer you high-quality details and can do the final reviews.
• Dedicate a Team: It is better to have experienced people working on the project. It will increase the annotation accuracy and ensure everyone is on the same page regarding the relevancy.
• Diversification: Getting people from different backgrounds, abilities, and expertise will ensure no systematic bias.

We have listed the four best practices for handling the quality assurance process to make things easy. Bloomberg’s Global Data department collects these practices. Here is a table explaining the quality assessment methods, their benefits, and their drawbacks.