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What You Should Know About Data Annotation

Businesses accumulate large amounts of data daily, most of which they do not utilize because it is unstructured. They need data to make informed decisions and simplify their operations. While it might not be obvious, algorithms affect modern life. For example, you use a GPS app to estimate arrival time. Streaming sites use them to arrange their streaming queues. These apps use AI and machine learning algorithms, and more people rely on them for efficiency and personalization. However, you cannot achieve these automated functions without data annotation, which accurately labels datasets to train machines and gain artificial intelligence. 

Data annotation 

Undoubtedly, almost every organization today needs data annotation. The process is necessary for machine learning. Data annotation is the only way the machine can get all the data it needs to learn. Without it, you cannot achieve success in implementing machine learning and artificial intelligence. 

However, since a machine learns from its data, the annotation should be accurate. Several platforms and tools for data annotation are now available to help humans label datasets. The machine model should receive enough annotated data to detect repeating patterns, which allow the machine to learn and eventually make predictions in the future on its own.  

Types of data annotation 

Data annotation is a comprehensive system that encompasses several data types that require different labeling processes. Some of the common types include the following:

  • Semantic annotation. This process focuses on concepts like company names, places, or people that annotators label within a text. It helps the machine model categorize new concepts it can use in other texts. It helps train AI to improve search relevance and make chatbots better.
  • Image annotation. The process ensures that the learning model recognizes an annotated area as a separate and unique object. Image annotation requires bounding boxes and semantic segmentation (giving meaning to every pixel). The annotation is helpful in facial recognition software and guides for autonomous vehicles.
  • Video annotation. Video annotation also uses bounding boxes frame-by-frame. The process helps the machine learn movement. In addition, video annotation helps teach machines about object tracking and localization.
  • Audio annotation. This annotation process deals with tags for speeches and audio parts in videos.

Text categorization 

This type of annotation assigns categories to paragraphs or sentences by topic within a document.

  • Entity annotation. Annotators help teach the machine to understand unstructured sentences to ensure that its understanding is more profound. For example, the annotator can use Named Entity Recognition (NER). The words within the text document get annotations from predetermined categories in this process. For example, they label the word as a thing, place, or person. Otherwise, the annotators can also use entity linking, where they tag parts of the text as related, for example, a company and its address or location.  
  • Intent extraction. In this process, the annotator tags the sentences or phrases with intent so that the machine can build a library of the different ways people use specific words, which help in training chatbots. For example, the sentences “Can I change my reservation?” and “When can I receive confirmation of my reservation?” have the same keyword, but the intent is different.

Why is data annotation critical?

  • Data annotation allows a deeper understanding of the objects’ meanings, which helps improve the performance of algorithms.
  • Data annotation improves the AI and ML models. Skilled annotators provide accurate labels to various forms of data. The quality of the annotation affects the precision of the machine’s learning.
  • AI and ML models only understand what it needs to know or do through the properly labeled data the annotators feed them. High-quality data annotation allows the machine to learn faster.
  • You can easily create labeled datasets, as data annotation streamlines the preprocessing of the data. Using a data annotation service, for example, leads to creating a vast amount of labeled datasets that AI and ML models can use for specific purposes.
  • Many tools and devices for various industries, such as healthcare, finance, transportation, advertising, and manufacturing, benefit from AI and machine learning. Examples include self-driving cars, smart assistants, virtual travel booking agents, conversational marketing bots, etc.

Data annotation is a data analysis tool that can help businesses move forward. However, it is not an easy project and requires skills and expertise. The process includes data collection, categorization, and data processing. 

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