What is Data Annotation? What are the Types of Data Annotation?

14 December 2021

According to Forbes, most of the data in the digital world—around 90% of it—is unstructured. Data-driven organizations consistently utilize data in the decision-making process to boost their productivity and bring effective change. To grasp the full potential and gain actionable insight from unstructured data organizations must scrutinize a mountain of detail.

Artificial Intelligence has been applied to the challenge, but for an AI to identify its cluttered environment and take real-life actions, it requires quality training datasets; organizations must label the datasets accurately to allow the AI to understand easily. It is achieved through a process known as data annotation.

When it comes to training the AI it is all about the basics. Data annotation is like using flashcards with toddlers to introduce texture, shapes, and colors. Once the basics are rock solid, toddlers can build on that and learn as they grow.

Likewise, AI can self-learn and become intelligent over time with quality datasets. Therefore, developers prefer human-powered annotation to acquire high-standard data annotation. Human annotated data are considered error-free as they are done through unbiased and precise procedures. In general, annotation helps enhance the speed, efficiency, and security of computer systems. What are the types of data annotations and the need for data annotation across industries?

General Text Annotation—Calibrate strategic business decisions

An incredible amount of information can be conveyed in a single block of text. Sometimes, these texts are unstructured and difficult to comprehend. Text annotation helps organizations categorize these texts for machines to identify the individual element in the given text content. Some of the techniques used for text annotation are sentiment annotation, intent annotation, and entity annotation. The project the organization has embarked on—and the relevant use case—decide the suitable type of text annotation technique.

  • Sentiment annotation analysis—identifies[MOU1]  the emotions of a text by labeling the text as positive, negative, or neutral.
  • Intent annotation analysis—classifies the desire behind the text into several categories such as request, confirmation, contradiction, command, rejection, etc.
  • Entity recognition—identifies and tags various entities in the given text—for example, name, place, or a person in the sentence.
  • Text annotation—helps specify the relation between multiple parts of the sentences and labels expressions or phrases.

Medical Text Annotation for Improved medical care

The health industry is a good example of where text annotation can be applied. Clinical information of a patient is crucial for diagnosis and treatment. To retrieve patient information from a voluminous amount of unstructured clinical data is time-consuming and inefficient. Medical text annotation annotates medical entity types and their relations such as procedures, drugs, illness, contextual information, and patient vitals. Importantly, it aids AI-enabled medical devices to speed up the process of medical diagnosis.

AI-augmented medical devices and applications help the healthcare sector enhance patient care. To improve the AI accuracy and efficacy expert medical text annotation is a key tool to apply.

On a broader level, medical text annotation is also a necessary effort to build a highly advanced AI-enabled healthcare technology. The training dataset derived from medical text annotation is critical for educating a quality AI to help medical professionals with more accurate results.

General Image Annotation for classification accuracy— Pixel-level annotation

Humans have an amazing capability to perceive and differentiate objects and images as they are presented, for example in a photograph. On the other hand machines cannot recognize objects unless they know the elements in the image; they need help. Image annotation is more than just drawing a box around objects in the picture. Annotators have a set of specifications and guidelines to label the images. A simple example would be annotating the type of vehicles in the image. The idea is for AI to self-learn and classify vehicles.

General image annotation techniques include bounding boxes, image classification, lines and splines, polygons, and semantic segmentation. To carry out an image annotation you need three things: the image, domain experts, and an annotation platform.

Medical Image Annotation—Enhance healthcare with medical AI.

Over the past decade, the medical AI industry has seen the growth of 50 new startups receiving upwards of $8.5 billion in seed money. AI is transforming the healthcare industry for good, and for physicians facilitating their use big data to make precise diagnoses.

In general, diagnosis takes time, but implementing AI offers real-time insight into radiology and pathology, thereby reducing the time taken for diagnosis and treatment. Another huge benefit is the facility to compare previous medical images with the present to detect changes—either positive or degenerative. For example, it may take practitioners weeks to make a proper diagnosis from the radiology and pathology reports, but fortunately medical image annotation can help AI detect images with accuracy, from simple blood work to autoimmune diseases.  AI medical applications help physicians to offer timely diagnoses and essential treatments.

What PreludeSys can do for you?

PreludeSys is a leading digital transformation partner that offers data annotation services for healthcare organizations that focus on building better patient care through AI-enabled devices. Over the years, we have provided successful data annotation services to a wide set of clients. We understand your needs and deliver quality outcomes for your organization.

Gain accurate predictions with fast-performing AI. Obtain high-quality training datasets suiting your business needs. Contact our expert team for a detailed demo.



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