Machine Learning Essentials: What is Data Annotation?
Data annotation helps machines make sense of text, video, image or audio data.
One of the stand-out characteristics of Artificial Intelligence (AI) is its ability to learn, for better or for worse. It’s this ongoing effort that distinguishes AI from static, code-dependent software.
It’s also precisely this ability that makes high-quality annotated data a crucial element in training representative, successful, and bias-free AI models.
Data annotation is the process of labeling individual elements of training data (whether text, images, audio, or video) to help machines understand what exactly is in it and what is important. This annotated data is then used for model training. Data annotation also plays a part in the larger quality control process of data collection, as well—annotated datasets become ground truth datasets: data that is held up as a gold standard and used to measure model performance and the quality of other datasets.
Teaching Through Data
The purpose of annotating data is to tell machine learning models exactly what we want them to know. Teaching a machine to learn through annotation can be likened to teaching a toddler shapes and colors using flashcards, where the annotations are the flashcards and annotators are the teacher.
Of course, this is a simplified example of how AI learns. In practice, machine learning models need large volumes of correctly annotated data to learn how to perform a task – which can prove to be a challenge in practice. Companies must have the resources to collect and label data for their specific use case—sometimes in a less-resourced language or dialect.
The following is a closer look at the different types of data annotation, how annotated data is used, and why humans will continue to be an indispensable part of the data annotation process in the future.
The Importance of Data Annotation
The caliber of your input data will determine how well your machine learning models perform. And for this to happen, data annotation plays a key role in helping your models understand the requirements in the right way.
Before we dive into data annotation any further, let us look at the types of data that define the role of annotating data. Primarily, data around us is classified into two categories: structured and unstructured data. Structured data comes with a pattern that is clearly identifiable and searchable by computers, while unstructured data, despite having an internal structure humans can understand, lacks those patterns. Examples of unstructured data include social media posts, emails, text files, phone recordings and chat communications, and more. Both human and automated processes can produce unstructured data. This unstructured data is expanding exponentially, and organizations continue to struggle to process and extract value from it. Defined.ai strives to address this lack of structured training data for machine learning.
Data annotation is especially important when considering the amount of unstructured data that exists in the form of text, images, video, and audio. By most estimates, unstructured data accounts for 80% of all data generated.
Currently, most models are trained via supervised learning, which relies on well-annotated data from humans to create training examples.
Types of Data Annotation
Because data comes in many different forms, there are several different types of data annotation, for either text, image or video-based datasets. Here is a breakdown each of these three types of data annotation.
The Written Word: Text Annotation
There is an incredible amount of information within any given text dataset. Text annotation is used to segment the data in a way that helps machines recognize individual elements within it. Types of text annotation include:
Named Entity Tagging: Single and Multiple Entities:
Named Entity Tagging (NET) and Named Entity Recognition (NER) help identify individual entities within blocks of text, such as “person,” “sport,” or “country.”
This type of data annotation creates entity definitions, so that machine learning algorithms will eventually be able to identify that “Saint Louis” is a city, “Saint Patrick” is a person, and “Saint Lucia” is an island.
Humans use language in unique and varying ways to express thoughts through phrases that can’t always be taken at face value. Therefore, it’s necessary to read between the lines or consider the context to understand the sentiment behind a phrase. This is why sentiment tagging is crucial in helping machines decide if a selected text is positive, negative, or neutral.
In many cases, the sentiment of a sentence is clear: for example, “Super helpful experience with the customer support team!” is clearly positive. However, when the intent is less straightforward or when sarcasm or other ambiguous speech is used, it becomes more difficult to discern the true meaning. For example, “Great reviews for this place, but I can’t say I agree!” This is where human annotation adds real value.
The intent or meaning of words can vary greatly depending on the context and within specific domains. For example, domain-specific jargon used in a technical conversation in the finance industry is very different from the one used in the telecommunications industry, or the slang used between two friends. Semantic annotation gives that extra context that machines need to truly understand the intent behind the text.
More than Meets the Eye: Image Annotation
Image annotation helps machines understand what elements are present within an image. This can be done by using Image Bounding Boxes (IBB), in which elements of an image are labeled with basic bounding boxes, or through more advanced object tagging.
Annotations in images can range from simple classifications (labeling the gender of people in an image, for example) to more complex details (for example, labeling whether the scene is rainy or sunny). Image classification is another approach where images are annotated based on single or multi-level categories. In this case, an example would be images of mountains classified into “Mountain” category.
Movement Detected Video annotation
Video annotation works in similar ways to image annotation – using Bounding Boxes and other annotation methods, single elements within frames of a video are identified, classified, or even tracked across multiple frames. Video annotation works in similar ways to image annotation – using bounding boxes and other annotation methods, single elements within frames of a video are identified, classified, or even tracked across multiple frames. For example, tagging all the humans in a Closed-Circuit Television (CCTV) video as “Customer” or helping autonomous vehicles recognize objects along the road.
Important Notes on Data Annotation
Human vs. Machine
Humans play an integral role in ensuring that data is annotated properly. Humans can provide context and a deeper understanding of intent in creating ground truth datasets, enhancing annotations’ overall value.
In-house versus outsourcing
Data annotation is essential but also resource-heavy and time-consuming.
One report showed that data preparation and engineering tasks represent over 80% of the time spent on most machine learning projects. Organizations may often be faced with the decision of whether to perform data annotation in-house or to outsource it.
There are some advantages to performing data annotation in-house. For one, you retain control and visibility over the data collection process. Secondly, with very niche or technical models, subject matter experts with relevant knowledge may already be in-house.
However, outsourcing data annotation to a third party is an excellent solution to some of the biggest challenges to doing data annotation in-house, namely time, resources, and quality. Third-party data annotation can help reach the scale, speed, and quality needed to create effective training datasets while complying with increasingly complex data privacy rules and requirements.
Making Your Machine Smarter
Data annotation is key to the data collection process and essential in helping machines reach their full potential. Consistent, high-quality output becomes possible by feeding these models with accurately annotated datasets, insights, and predictions.
To learn more about our data annotation services, visit us here.