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data-annotation

What is data annotation?

Machine learning systems need to process correctly annotated data to learn how to recognise and process new patterns of behaviour. But what exactly does annotation work entail?

 

By annotation we mean the labelling of various types of content (texts, audio, images, video) aimed at training machines more easily to process input.

Thanks to the combination of automatic processes and human intervention, data are organised into well-defined categories and prepared for annotation. Every business sector demands specific requirements for this type of activity; there is actually an increasing number of companies that now rely on external data providers in order to offer suitable and high-quality services.

How many types of data annotation are there?

 

TEXT ANNOTATION

Text annotation is still the most widespread activity used by businesses that develop AI projects. It consists of linking mega data tags to specific words, sentences or entire sections of text. In this way, machines learn to recognise and quickly comprehend emotions and human intent through word analysis. This enables digital customer service systems to interact more naturally with users through messages and to find swift solutions to their problems.

The human-in-the-loop approach in this case is particularly useful for sentiment analysis. In fact, contributors can support machines in recognising the emotions of internet users, providing a concrete assistance in this activity. This is how AI systems learn to accurately understand user requests and they are able to respond appropriately to the customers by fulfilling their requests.

Semantic annotation, on the other hand, consists of the schematic representation of the various components of a text, in the attempt to train machines to have an in-depth understanding of its meaning. By recognising the various parts of the text, algorithms successfully and quickly obtain a comprehensive view of the whole document.

AUDIO ANNOTATION

Annotation makes every format of audio data comprehensible to machine learning models. This process involves carefully listening to sound and attributing metadata to the individual characteristic parts of the audio. In this way the audio files are split apart, labelled and made more accessible to machines for the development of various projects. The same process can also help detect the language or dialect of the speaker in relation to several environmental contexts. In most cases, we rely on audio annotation for the development of voice recognition models, chatbots and virtual assistant devices. The annotation of audio data consists of transcribing significant elements from an audio track, and this allows us to identify some differences in the pronunciation and intonation of whoever is speaking. In more general terms, the same process can also help detect the language or dialect of the speaker in relation to several environmental contexts. The recognition of all these elements by the algorithms becomes essential in developing voice assistants, which require the greatest level of comprehension of the audio context.

IMAGE AND VIDEO ANNOTATION

The annotation of images and video refers to the association of metadata with individual parts of an image or a video file, capable of describing them in detail through keywords and specific characterisations. This type of annotation can be applied to a variety of contexts and sectors for the development of computer vision systems, facial recognition systems and other solutions based on image recognition. This annotation work improves precision and accuracy specifically in terms of recognition by automatic systems.

How important is human intervention in data annotation?

Machines do not always manage to understand the more subjective nuances coming from the data. This is why human intervention is, in the end, fundamental. Not only for recognising emotions but also for checking the quality of the annotation work as a whole.

Thanks to an international network of contributors, Creative AI is able to exceed the limits of algorithms and provide a complete and reliable data annotation service for every industry. Are you developing an AI project and need to train your machine learning models with annotated data? Contact us!