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Data Annotation: what it is and difference with Data Labeling

data annotation - Eurotrad

Artificial Intelligence would not exist without training in the use and identification of data and patterns. The ability of machines to acquire information automatically is directly linked to the way data is entered and labelled. Data Annotation plays a key role in the field of artificial intelligence and machine learning, offering a number of significant advantages in the field of neural machine translation as well.
When it comes to Data Annotation, the meaning of this expression is often confused with that of Data Labelling. In fact, there is a subtle but substantial difference between the two concepts. Let us understand better what Data Annotation Services for Machine Learning are and why they should not be confused with Data Labelling.

What is Data Annotation?

Both Data Annotation and Data Labelling are fundamental to the training of Machine Learning models. Although they are similar, the Data Annotation service is a broader process that can include data labelling, but also goes beyond this by providing a more in-depth context and understanding of the data.
Unlike Data Labelling, therefore, Data Annotation or the 'data annotation service' is the process of attributing data to help algorithms better understand the information they process.

To better understand what data annotation is, it is important to remember that the process can be:

  • manual
  • semi-automatic
  • automatic

In manual data annotation, people examine and annotate the data: a process that can be time-consuming and demanding but provides high-quality results. Semi-automatic annotation, on the other hand, combines human intervention with automation that speeds up the process, while automatic annotation uses Machine Learning algorithms to perform the Data Annotation service with no human intervention.

Differences between Data Annotation and Data Labelling

Data cannot be entered randomly but must always be labelled to help the models understand, process and learn information independently. Data Labelling, or the labelling of information or metadata, is used to selectively classify the whole wealth of information, be it images, audio, video, text or simple data, which is used to develop Artificial Intelligence.
The term Data Labelling is often used interchangeably with Data Annotation. However, there are some key differences.
Both processes involve the addition of information, but Data Labelling refers to the act of assigning categorical labels to data. For example, in a dataset of animal images, data labelling might consist of assigning each image a label such as 'dog', 'cat' or 'turtle'. Instead, what is data annotation? Data annotation, on the other hand, is a broader process that may include the addition of other information such as, to use the same example, in the animal categories, the environment in which the animal is located or the action it is performing. All this information will help the Machine Learning model to understand and interpret the data more effectively.

Eurotrad's Data Annotation service: the advantages

One of the main benefits of Eurotrad’s Data Annotation service is its ability to improve the accuracy and effectiveness of Machine Learning models, as annotations provide valuable context that can help the model make more accurate predictions.
For example, in the field of neural machine translation, data annotation can be used to provide contextual information that helps the system better understand the meaning of a sentence and produce a more accurate translation. Another advantage of our service is the ability to reduce bias in Machine Learning models, as careful data annotation ensures that the model is trained on a balanced and representative dataset.
Finally, Data Annotation can also help make Machine Learning models easier to interpret. Annotations, in fact, provide valuable information on why a model made a certain prediction, making it easier for people to understand and consequently trust it.

Redazione Eurotrad

November 13, 2023

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