What is Data Labeling?

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Data labeling, or data annotation, is a crucial process in machine learning where descriptive labels are assigned to raw data to provide context. These labels enable machine learning models to accurately interpret data and make predictions. Data labeling forms the basis of supervised learning, allowing models to learn from labeled examples and generalize patterns to new data. Accuracy and quality in data labeling are essential for effective machine learning outcomes, making it a foundational step in ML workflows.

The Process of Data Labeling

Data labeling encompasses the identification of raw data, such as images or text files, and the addition of descriptive labels to indicate their context. This process lays the foundation for various ML and deep learning applications, including computer vision and natural language processing.

How Data Labeling Works

Companies employ a combination of software tools, procedural workflows, and human annotators to carry out data labeling tasks. Human-in-the-loop (HITL) participation ensures the quality and accuracy of labeled data, guiding the ML model training process effectively. 

Here you can find an article on the best image labeling tools for Computer Vision and here you can read about the best free labeling tools for text annotation in NLP

Labeled Data vs. Unlabeled Data

Labeled data, essential for supervised learning, is characterized by the presence of explicit labels that guide ML models during training. In contrast, unlabeled data lacks such annotations and is typically used in unsupervised learning scenarios. Labeled data is more resource-intensive to acquire and store but provides actionable insights for ML tasks. For more details, see: explanation of supervised vs. unsupervised learning.

Benefits and Challenges

Data labeling offers numerous benefits, including more precise predictions and better data usability for ML models. However, it also presents challenges such as cost, time consumption, and the potential for human errors. Quality assurance measures are essential to mitigate these challenges and ensure the accuracy of labeled data.

Best Practices in Data Labeling

Adhering to best practices is crucial for optimizing the accuracy and efficiency of data labeling processes. Intuitive task interfaces, consensus measures, label auditing, transfer learning, and active learning techniques are some of the recommended practices for improving data labeling outcomes.

Data Labeling at dida

At dida, a German AI service provider, we have a strong opinion on data labeling: 

It is best to first work on a high-quality data labeling schema, so a system, where the domain experts and the machine learning scientists jointly define, which aspects are crucial to be labeled and which fine-grained details are important. Then, once the labeling schema is well-defined, at dida, we start labeling: first our ML scientists themselves and then mainly with the help of our internal data labeling students. We prefer to label data in-house, as we have more control over the labeling quality and it is easier to adapt the labeling schema. 

Take this computer vision, remote sensing example of 4 persons labeling the same rooftop for our rooftop segmentation solution: All 4 persons created their labels differently. Person 1 did a good job. Person 2 forgot some obstacles on the roofs. Person 3 drew his / her labels not precisely and not with edges and person 4 completely failed the task. 

If you take a look at this NLP project example, you realize once again that defining a label is not always trivial. In this case, it is unclear, whether the volume or amount label should only involve the number, or the number plus the unit of measurement, or even the German term “Inhalt” (which refers to the “content”) or “Menge” (which refers to the “amount”). To be clear: There is no right or wrong answer. Finding a good labeling schema is something that needs to be tested and iterated together with Machine Learning specialists especially the domain experts of a respective project.  

Here you can see what an NLP labeling tool can look like. For this project, we labeled legal paragraphs for an AI solution for legal contracts.

Conclusion

In summary, data labeling is a critical component in the ML pipeline, providing the necessary context for training accurate and reliable ML models. By understanding the intricacies of data labeling and adopting best practices, organizations can harness the full potential of their data assets to drive innovation and achieve business objectives in the era of AI.

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At dida, we’re a highly specialized team, working on implementing complex AI projects for 

medium and large-size enterprises. We’re regularly publishing our own AI research at the most renowned international conferences (such as NeurIPS, ICML, or ICLR) and are internationally awarded by Microsoft or UNESCO for our AI solutions.