What is data annotation and why does it matter?
Daily life is guided by algorithms. Even the simplest decisions — an estimated time of arrival from a GPS app or the next song in the streaming queue — can filter through artificial intelligence and machine learning algorithms. We rely on these algorithms for a number of different reasons which include personalization and efficiency. But their ability to deliver on these promises is dependent on data annotation: the process of accurately labeling datasets to train artificial intelligence to make future decisions. Data annotation is the workhorse behind our algorithm-driven world.
What is data annotation?
Computers can’t process visual information the way human brains do: A computer needs to be told what it’s interpreting and provided context in order to make decisions. Data annotation makes those connections. It’s the human-led task of labeling content such as text, audio, images and video so it can be recognized by machine learning models and used to make predictions.
Data annotation is both a critical and impressive feat when you consider the current rate of data creation. By 2025, an estimated 463 exabytes of data will be created globally on a daily basis, according to The Visual Capitalist — and that research was done before the COVID-19 pandemic accelerated the role of data in daily interactions. Now, the global data annotation tools market is projected to grow nearly 30% annually over the next six years, according to GM Insights, especially in the automotive, retail and healthcare sectors.
Why does it matter?
Data is the backbone of the customer experience. How well you know your clients directly impacts the quality of their experiences. As brands gather more and more insight on their customers, AI can help make the data collected actionable. According to Gartner, by 2022, 70% of customer interactions are expected to filter through technologies like machine learning (ML) applications, chatbots and mobile messaging.
“AI interactions will enhance text, sentiment, voice, interaction and even traditional survey analysis,” says Gartner vice-president Don Scheibenreif on the analyst firm’s blog. But in order for chatbots and virtual assistants to create seamless customer experiences, brands need to make sure the datasets guiding these decisions are high-quality.
As it currently stands, data scientists spend a significant portion of their time preparing data, according to a survey by data science platform Anaconda. Part of that is spent fixing or discarding anomalous/non-standard pieces of data and making sure measurements are accurate. These are vital tasks, given that algorithms rely heavily on understanding patterns in order to make decisions, and that faulty data can translate into biases and poor predictions by AI .
AI starts with data: Facing the challenges of data collection & annotation
Discover useful insights into the challenges of data preparation to ensure that your next artificial intelligence project is a success.
Types of data annotation
Data annotation is a broad practice but every type of data has a labeling process associated with it. Here are some of the most common types:
- Semantic annotation: Semantic annotation is a process where concepts like people, places or company names are labeled within a text to help machine learning models categorize new concepts in future texts. This is a key part of AI training to improve chatbots and search relevance.
- Image annotation: This type of annotation ensures that machines recognize an annotated area as a distinct object and often involves bounding boxes (imaginary boxes drawn on an image) and semantic segmentation (the assignment of meaning to every pixel). These labeled datasets can be used to guide autonomous vehicles or as part of facial recognition software.
- Video annotation: Similar to image annotation, video annotation uses techniques like bounding boxes but on a frame-by-frame bases, or via a video annotation tool, to acknowledge movement. Data uncovered through video annotation is key for computer vision models that conduct localization and object tracking.
- Text categorization: Text categorization is the process of assigning categories to sentences or paragraphs by topic, within a given document.
- Entity annotation: The process of helping a machine to understand unstructured sentences. There are a wide variety of techniques that can be utilized to establish a greater understanding such as Named Entity Recognition (NER) , where words within a body of text are annotated with predetermined categories (e.g., person, place or thing). Another example is entity linking , where parts of a text (e.g., a company and the place where it’s headquartered) are tagged as related.
- Intent extraction: Intent extraction is the process of labeling phrases or sentences with intent in order to build a library of ways people use certain verbiage. For example, “How do I make a reservation?” and “Can I confirm my reservation,” both contain the same keyword, but have different intent. It’s another key tool for teaching chatbot algorithms to make decisions about customer requests.
- Phrase chunking: phrase chunking involves tagging parts of speech with its grammatical definition (e.g., noun or verb).
An evolving science
In the same way that data is constantly evolving, the process of data annotation is becoming more sophisticated. To put it in perspective, four or five years ago, it was enough to label a few points on a face and create an AI prototype based on that information. Now, there can be as many as 20 dots on the lips alone.
The ongoing transition from scripted chatbots to conversational AI is one of the frontrunners promising to bridge the gap between artificial and natural interactions. At the same time, consumer trust in AI-derived solutions is gradually increasing. A recent study published in Harvard Business Review found that people were far more likely to accept an algorithm’s recommendations when it comes to a product’s practicality or objective performance.
Algorithms will continue to shape consumer experience for the foreseeable future — but algorithms can be flawed, and can suffer from the same biases of their creators . Ensuring AI-powered experiences are pleasant, efficient and effective requires data annotation done by diversified teams with a nuanced understanding of what they’re annotating. Only then can we ensure data-based solutions are as accurate and representative as possible.
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Data Annotation & Its Role in Machine Learning
Data annotation plays an essential role in the world of machine learning . It is a core ingredient to the success of any AI model because the only way for an image detection AI to detect a face in a photo is if many photos already labelled as “face” exist.
If there is no annotated data, there is no machine learning model.
What is data annotation?
The core function of annotating data is to label data. Labeling data is among the first steps in any data pipeline . Plus, the act of labeling data often results in cleaner data and additional areas of opportunity.
Labeling data
It is necessary to have two things when annotating data:
- A consistent naming convention
As labeling projects grow more mature, the labeling conventions likely increase in complexity.
Sometimes, too, after training a model on the data, you might discover that the naming convention was not sufficient to create the kind of predictions or ML model you intended. Now you need to get back to the drawing board and redesign the tags for the dataset.
Clean data builds more reliable ML models . To measure if the data is clean:
- Test the data for outliers.
- Test data for missing values or null values.
- Ensure labels are consistent with conventions.
Annotation can help make a dataset cleaner. It can fill in gaps where there are some. When exploring the dataset, it might be possible to find bad data and data outliers. Data annotation can both:
- Salvage poorly tagged data or data with missing labels
- Create new data for the ML model to use
Automated vs human annotation
Data annotation can be costly, depending on the method.
Some data can be automatically annotated , or, at least annotated through automated means with a degree of accuracy. For example, here are simple forms of annotation:
- Googling an image of a horse and downloading the top 1,000 photos into a horse file.
- Scraping a media site for all its sports content and labeling all the articles as sports articles.
You’ve automatically collected horse and sports data, but the degree of accuracy of that data is unknown until investigated. It’s possible some horse photos downloaded are not actual photos of horses, after all.
Automation saves costs but risks accuracy. In contrast, human annotation can be much more costly, but it’s more accurate.
Data annotators can annotate data to the specificity of their collected knowledge. If it is a horse photo, the human can confirm it. If the person is an expert in horse breeds, the data can be further annotated to the specific breed of the horse. It’s even possible for the person to draw a polygon around the horse in the picture to annotate exactly which pixels are the horse.
For the sports articles, the article could be broken down to which sport, a game report, player analysis, or game predictions. If the data is tagged only as sports, the annotation has less specificity.
In the end, data is annotated to both:
- A degree of specificity
- A degree of accuracy
Which is more necessary, however, always depends on how the machine learning problem is defined.
Human-in-the-loop learning
In IT, the “distributed” mentality is the idea of pushing workloads to a single instance to eliminate huge amounts of work piling in a single location. This is true of the Kubernetes architecture , computer processing infrastructure, edge AI concepts , microservices architecture—and it is true of data annotation.
Data annotation can be cheaper, even free, if the annotation can occur in the user’s process.
It is a boring and unfulfilling job to offer someone to sit and tag data all day. But, if the labelling can occur naturally within the user experience or once in a while from many people instead of one, then the job becomes a lot more approachable and the possibility of getting annotations is even achievable.
This is known as human-in-the-loop (HITL) , and it’s often one function of mature machine learning models.
For example, Google has included HITL and data annotation in its Google Docs software. Every time a user clicks the word with the squiggly line beneath it and selects a different word or a spell-corrected word, Google Docs gets a tagged piece of data that confirms the predicted word is a correct replacement for the word with the error.
Google Docs has included its user as a part of the process by making a simple feature in its app to get annotated data, real-world data form its users.
In this way, Google sort of crowd-sources its data annotation problem and doesn’t have to hire teams of people to sit at a desk all day reading misspelled words.
Tools for data annotation
Annotation tools are tools designed to help annotate pieces of data. The data they accept are:
The tools generally have a UI that allows you to make annotations simply and export the data in various forms. The exported data can be returned as a .CSV file, text document, file of marked photos, or they can even format the annotated data into a JSON format specific to the convention for training that data in a Machine Learning model.
These are two well-known annotation tools:
- Label Studio
But that’s not nearly all of them. Awesome-data-annotation is a GitHub repository with an excellent list of data annotation tools to use.
Data annotation is an industry
Data annotation is essential to AI and machine learning, and both have added immense value to the world.
To continue growing the AI industry, data annotators are needed, so the job is sticking around. Data annotation is already an industry and will only continue to grow as more and more nuanced datasets are required to build out some of machine learning’s more nuanced problems.
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Data Annotation Tutorial: Definition, Tools, Datasets
Data is an integral part of all machine learning and deep learning algorithms .
It is what drives these complex and sophisticated algorithms to deliver state-of-the-art performances.
If you want to build truly reliable AI models , you must provide the algorithms with data that is properly structured and labeled.
And that's where the process of data annotation comes into play.
You need to annotate data so that the machine learning systems can use it to learn how to perform given tasks.
Data annotation is simple, but it might not be easy 😉 Luckily, we are about to walk you through this process and share our best practices that will save you plenty of time (and trouble!).
Here’s what we’ll cover:
What is data annotation?
Types of data annotations.
- Automated data annotation vs. human annotation
V7 data annotation tutorial
Solve any video or image labeling task 10x faster and with 10x less manual work.
Don't start empty-handed. Explore our repository of 500+ open datasets and test-drive V7's tools.
And hey—if you are ready to start annotating your training data, check out:
- How to Split Your Machine Learning Data
- Image annotation with V7
- Video annotation with V7
- Model-assisted labeling with V7
Essentially, this comes down to labeling the area or region of interest—this type of annotation is found specifically in images and videos. On the other hand, annotating text data largely encompasses adding relevant information, such as metadata, and assigning them to a certain class.
In machine learning , the task of data annotation usually falls into the category of supervised learning, where the learning algorithm associates input with the corresponding output, and optimizes itself to reduce errors.
Here are various types of data annotation and their characteristics.
Image annotation
Image annotation is the task of annotating an image with labels. It ensures that a machine learning algorithm recognizes an annotated area as a distinct object or class in a given image.
It involves creating bounding boxes (for object detection ) and segmentation masks (for semantic and instance segmentation) to differentiate the objects of different classes. In V7, you can also annotate the image using tools such as keypoint, 3D cuboids, polyline, keypoint skeleton, and a brush.
💡 Pro tip: Check out 13 Best Image Annotation Tools to find the annotation tool that suits your needs.
Image annotation is often used to create training datasets for the learning algorithms.
Those datasets are then used to build AI-enabled systems like self-driving cars, skin cancer detection tools, or drones that assess the damage and inspect industrial equipment.
💡 Pro tip: Check out AI in Healthcare and AI in Insurance to learn more about AI applications in those industries.
Now, let’s explore and understand the different types of image annotation methods.
- Bounding box
The bounding box involves drawing a rectangle around a certain object in a given image. The edges of bounding boxes ought to touch the outermost pixels of the labeled object.
Otherwise, the gaps will create IoU (Intersection over Union) discrepancies and your model might not perform at its optimum level.
💡 Pro tip: Read Annotating With Bounding Boxes: Quality Best Practices to learn more.
The 3D cuboid annotation is similar to bounding box annotation, but in addition to drawing a 2D box around the object, the user has to take into account the depth factor as well. It can be used to annotate objects such on flat planes that need to be navigated, such as cars or planes, or objects that require robotic grasping.
You can annotate with cuboids to build to train the following model types:
- Object Detection
- 3D Cuboid Estimation
- 6DoF Pose Estimation
Creating a 3D cuboid in V7 is quite easy, as V7's cuboid tool automatically connects the bounding boxes you create by adding a spatial depth. Here's the image of a plane annotated using cuboids.
While creating a 3D cuboid or a bounding box, you might notice that various objects might get unintentionally included in the annotated region. This situation is far from ideal, as the machine learning model might get confused and, as a result, misclassify those objects.
Luckily, there's a way to avoid this situation—
And that's where polygons come in handy. What makes them so effective is their ability to create a mask around the desired object at a pixel level.
V7 offers two ways in which you can create pixel-perfect polygon masks.
a) Polygon tool
You can pick the tool and simply start drawing a line made of individual points around the object in the image. The line doesn't need not be perfect, as once the starting and ending points are connected around the object, V7 will automatically create anchor points that can be adjusted for the desired accuracy.
Once you've created your polygon masks, you can add a label to the annotated object.
b) Auto-annotation tool
V7's auto-annotate tool is an alternative to manual polygon annotation that allows you to create polygon and pixel-wise masks 10x faster.
💡 Pro tip: Ready to train your models? Have a look at Mean Average Precision (mAP) Explained: Everything You Need to Know.
Keypoint tool
Keypoint annotation is another method to annotate an object by a series or collection of points.
This type of method is very useful in hand gesture detection, facial landmark detection, and motion tracking. Keypoints can be used alone, or in combination to form a point map that defines the pose of an object.
Keypoint skeleton tool
V7 also offers keypoint skeleton tool—a network of keypoints connected by vectors, used specifically for pose estimation.
It is used to define the 2D or 3D pose of a multi-limbed object. Keypoints skeletons have a defined set of points that can be moved to adapt to an object’s appearance.
You can use keypoint annotation to train a machine learning model to mimic human pose and then extrapolate their functionality for task-specific applications, for example, AI-enabled robots.
See how you can annotate your image and video data using the keypoint skeleton in V7.
💡 Pro tip: Check out 27+ Most Popular Computer Vision Applications and Use Cases.
Polyline tool
Polyline tool allows the user to create a sequence of joined lines.
You can use this too by clicking around the object of interest to create a point. Each point will create a line by joining the current point with the previous one. It can be used to annotate roads, lane marking, traffic signs, etc.
Semantic segmentation
Semantic segmentation is the task of grouping together similar parts or pixels of the object in a given image. Annotating data using this method allows the machine learning algorithm to learn and understand a specific feature, and it can help it to classify anomalies.
Semantic segmentation is very useful in the medical field, where radiologists use it to annotate X-Ray, MRI, and CT scans to identify the region of interest. Here's an example of a chest X-Ray annotation.
If you are looking for medical data, check out our list of healthcare datasets and see how you can annotate medical imaging data using V7.
Video annotation
Similar to image annotation, video annotation is the task of labeling sections or clips in the video to classify, detect or identify desired objects frame by frame.
Video annotation uses the same techniques as image annotation like bounding boxes or semantic segmentation, but on a frame-by-frame basis. It is an essential technique for computer vision tasks such as localization and object tracking.
Here's how V7 handles video annotation .
Text annotation
Data annotation is also essential in tasks related to Natural Language Processing (NLP).
Text annotation refers to adding relevant information about the language data by adding labels or metadata. To get a more intuitive understanding of text annotation let's consider two examples.
1. Assigning Labels
Adding labels means assigning a sentence with a word that describes its type. It can be described with sentiments, technicality, etc. For example, one can assign a label such as “happy” to this sentence “I am pleased with this product, it is great”.
2. Adding metadata
Similarly, in this sentence “I’d like to order a pizza tonight”, one can add relevant information for the learning algorithm, so that it can prioritize and focus on certain words. For instance, one can add information like “I’d like to order a pizza ( food_item ) tonight ( time )”.
Now, let’s briefly explore various types of text annotations.
Sentiment Annotation
Sentiment annotation is nothing but assigning labels that represent human emotions such as sad, happy, angry, positive, negative, neutral, etc. Sentiment annotation finds application in any task related to sentiment analysis (e.g. in retail to measure customer satisfaction based on facial expressions)
Intent Annotation
The intent annotation also assigns labels to the sentences, but it focuses on the intent or desire behind the sentence. For instance, in a customer service scenario, a message like “I need to talk to Sam ”, can route the call to Sam alone, or a message like “I have a concern about the credit card ” can route the call to the team dealing credit card issues.
Named Entity Annotation (NER)
Named entity recognition (NER) aims to detect and classify predefined named entities or special expressions in a sentence.
It is used to search for words based on their meaning, such as the names of people, locations, etc. NER is useful in extracting information along with classifying and categorizing them.
Semantic annotation
Semantic annotation adds metadata, additional information, or tags to text that involves concepts and entities, such as people, places, or topics, as we saw earlier.
Automated data annotation vs. human annotations.
As the hours pass by, human annotators get tired and less focused, which often leads to poor performance and errors. Data annotation is a task that demands utter focus and skilled personnel, and manual annotation makes the process both time-consuming and expensive.
That's why leading ML teams bet on automated data labeling.
Here's how it works—
Once the annotation task is specified, a trained machine learning model can be applied to a set of unlabeled data. The model will then be able to predict the appropriate labels for the new and unseen dataset.
Here's how you can create an automated workflow in V7.
However, in cases where the model fails to label correctly, humans can intervene, review, and correct the mislabelled data. The corrected and reviewed data can be then used to train the labeling model once again.
Automated data labeling can save you tons of money and time, but it can lack accuracy. In contrast, human annotation can be much more costly, but it tends to be more accurate.
Finally, let me show you how you can take your data annotation to another level with V7 and start building robust computer vision models today.
To get started, go ahead and sign up for your 14-day free trial.
Once you are logged in, here's what to do next.
1. Collect and prepare training data
First and foremost, you need to collect the data you want to work with. Make sure that you access quality data to avoid issues with training your models.
Feel free to check out public datasets that you can find here:
- 65+ Best Free Datasets for Machine Learning
- 20+ Open Source Computer Vision Datasets
Once the data is downloaded, separate training data from the testing data . Also, make sure that your training data is varied, as it will enable the learning algorithm to extract rich information and avoid overfitting and underfitting.
2. Upload data to V7
Once the data is ready, you can upload it in bulk. Here's how:
1. Go to the Datasets tab in V7's dashboard, and click on “+ New Dataset”.
2. Give a name to the dataset that you want to upload.
It's worth mentioning that V7 offers three ways of uploading data to their server.
One is the conventional method of dragging and dropping the desired photos or folder to the interface. Another one is uploading by browsing in your local system. And the third one is by using the command line (CLI SDK) to directly upload the desired folder into the server.
Once the data has been uploaded, you can add your classes. This is especially helpful if you are outsourcing your data annotation or collaborating with a team, as it allows you to create annotation checklist and guidelines.
If you are annotating yourself, you can skip this part and add classes on the go later on in the "Classes" section or directly from the annotated image.
💡 Pro tip: Not sure what kind of model you want to build? Check out 15+ Top Computer Vision Project Ideas for Beginners.
3. Decide on the annotation type
If you have followed the steps above and decided to “Add New Class”, then you will have to add the class name and choose the annotation type for the class or the label that you want to add.
As mentioned before, V7 offers a wide variety of annotation tools , including:
- Auto-annotation
- Keypoint skeleton
Once you have added the name of your class, the system will save it for the whole dataset.
Image annotation experience in V7 is very smooth.
In fact, don't believe just me—here's what one of our users said in his G2 review:
V7 gives fast and intelligent auto-annotation experience. It's easy to use. UI is really interactive.
Apart from a wide range of available annotation tools, V7 also comes equipped with advanced dataset management features that will help you organize and manage your data from one place.
And let's not forget about V7's Neural Networks that allow you to train instance segmentation, image classification , and text recognition models.
Unlike other annotation tools, V7 allows you to annotate your data as a video rather than individual images.
You can upload your videos in any format, add and interpolate your annotations, create keyframes and sub annotations, and export your data in a few clicks!
Uploading and annotating videos is as simple as annotating images.
V7 offers frame by frame annotation method where you can essentially create a bounding box or semantic segmentation per-frame basis.
Apart from image and video annotation , V7 provides text annotation as well. Users can take advantage of the Text Scanner model that can automatically read the text in the images.
To get started, just go to the Neural Networks tab and run the Text Scanner model.
Once you have turned it on you can go back to the dataset tab and load the dataset. It is the same process as before.
Now you can create a new bounding box class. The bounding box will detect text in the image. You can specify the subtype as Text in the Classes page of your dataset.
Once the data is added and the annotation type is defined you can then add the Text Scanner model to your workflow under the Settings page of your dataset.
After adding the model to your workflow map your new text class.
Now, go back to the dataset tab and send your data the text scanner model by clicking on ‘Advance 1 Stage’; this will start the training process.
Once the training is over the model will detect and read text on any kind of image, whether it's a document, photo, or video.
💡 Pro tip: If you are looking for a free image annotation tool, check out The Complete Guide to CVAT—Pros & Cons
Data annotation: next steps.
Nice job! You've made it that far 😉
By now, you should have a pretty good idea of what is data annotation and how you can annotate data for machine learning.
We've covered image, video, and text annotation, which are used in training computer vision models. If you want to apply your new skills, go ahead, pick a project, sign up to V7, collect some data, and start labeling it to build image classifier or object detectors!
💡 To learn more, go ahead and check out:
An Introductory Guide to Quality Training Data for Machine Learning
Simple Guide to Data Preprocessing in Machine Learning
Data Cleaning Checklist: How to Prepare Your Machine Learning Data
3 Signs You Are Ready to Annotate Data for Machine Learning
The Beginner’s Guide to Contrastive Learning
9 Reinforcement Learning Real-Life Applications
Mean Average Precision (mAP) Explained: Everything You Need to Know
A Step-by-Step Guide to Text Annotation [+Free OCR Tool]
The Essential Guide to Data Augmentation in Deep Learning
Nilesh Barla is the founder of PerceptronAI, which aims to provide solutions in medical and material science through deep learning algorithms. He studied metallurgical and materials engineering at the National Institute of Technology Trichy, India, and enjoys researching new trends and algorithms in deep learning.
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What is Data Annotation?
By Appen. July 10, 2020
Building an AI or ML model that acts like a human requires large volumes of training data . For a model to make decisions and take action, it must be trained to understand specific information. Data annotation is the categorization and labeling of data for AI applications. Training data must be properly categorized and annotated for a specific use case. With high-quality, human-powered data annotation, companies can build and improve AI implementations. The result is an enhanced customer experience solution such as product recommendations, relevant search engine results, computer vision, speech recognition, chatbots, and more. There are several primary types of data: text, audio, image, and video
Text Annotation
The most commonly used data type is text – according to the 2020 State of AI and Machine Learning report , 70% of companies rely on text. Text annotations include a wide range of annotations like sentiment, intent, and query.
Sentiment Annotation
Sentiment analysis assesses attitudes, emotions, and opinions, making it important to have the right training data. To obtain that data, human annotators are often leveraged as they can evaluate sentiment and moderate content on all web platforms, including social media and eCommerce sites, with the ability to tag and report on keywords that are profane, sensitive, or neologistic, for example.
Intent Annotation
As people converse more with human-machine interfaces, machines must be able to understand both natural language and user intent. Multi-intent data collection and categorization can differentiate intent into key categories including request, command, booking, recommendation, and confirmation.
Semantic Annotation
Semantic annotation both improves product listings and ensures customers can find the products they’re looking for. This helps turn browsers into buyers. By tagging the various components within product titles and search queries, semantic annotation services help train your algorithm to recognize those individual parts and improve overall search relevance.
Named Entity Annotation
Named Entity Recognition (NER) systems require a large amount of manually annotated training data. Organizations like Appen apply named entity annotation capabilities across a wide range of use cases, such as helping eCommerce clients identify and tag a range of key descriptors, or aiding social media companies in tagging entities such as people, places, companies, organizations, and titles to assist with better-targeted advertising content.
Real World Use Case: Improving Search Quality for Microsoft Bing in Multiple Markets
Microsoft’s Bing search engine required large-scale datasets to continuously improve the quality of its search results – and the results needed to be culturally relevant for the global markets they served. We delivered results that surpassed expectations. Beyond delivering project and program management, we provided the ability to grow rapidly in new markets with high-quality data sets. (Read the full case study here)
Audio Annotation
Audio annotation is the transcription and time-stamping of speech data, including the transcription of specific pronunciation and intonation, along with the identification of language, dialect, and speaker demographics. Every use case is different, and some require a very specific approach: for example, the tagging of aggressive speech indicators and non-speech sounds like glass breaking for use in security and emergency hotline technology applications.
Real World Use Case: Dialpad’s transcription models leverage our platform for audio transcription and categorization
Dialpad improves conversations with data. They collect telephonic audio, transcribe those dialogs with in-house speech recognition models, and use natural language processing algorithms to comprehend every conversation. They use this universe of one-on-one conversation to identify what each rep–and the company at large–is doing well and what they aren’t, all with the goal of making every call a success. Dialpad had worked with a competitor of Appen for six months but were having trouble reaching an accuracy threshold to make their models a success. It took just a couple weeks for the change to bear fruit for Dialpad and to create the transcription and NLP training data they needed to make their models a success. (Read the full case study here)
Image Annotation
Image annotation is vital for a wide range of applications, including computer vision, robotic vision, facial recognition, and solutions that rely on machine learning to interpret images. To train these solutions, metadata must be assigned to the images in the form of identifiers, captions, or keywords. From computer vision systems used by self-driving vehicles and machines that pick and sort produce, to healthcare applications that auto-identify medical conditions, there are many use cases that require high volumes of annotated images. Image annotation increases precision and accuracy by effectively training these systems.
Real World Use Case: Adobe Stock Leverages Massive Asset Profile to Make Customers Happy
One of Adobe’s flagship offerings is Adobe Stock, a curated collection of high-quality stock imagery. The library itself is staggeringly large: there are over 200 million assets (including more than 15 million videos, 35 million vectors, 12 million editorial assets, and 140 million photos, illustrations, templates, and 3D assets). Every one of those assets needs to be discoverable. Appen provided highly accurate training data to create a model that could surface these subtle attributes in both their library of over a hundred million images, as well as the hundreds of thousands of new images that are uploaded every day. That training data powers models that help Adobe serve their most valuable images to their massive customer base. Instead of scrolling through pages of similar images, users can find the most useful ones quickly, freeing them up to start creating powerful marketing materials. (Read the full case study here)
Video Annotation
Human-annotated data is the key to successful machine learning. Humans are simply better than computers at managing subjectivity, understanding intent, and coping with ambiguity. For example, when determining whether a search engine result is relevant, input from many people is needed for consensus. When training a computer vision or pattern recognition solution, humans are needed to identify and annotate specific data, such as outlining all the pixels containing trees or traffic signs in an image. Using this structured data, machines can learn to recognize these relationships in testing and production.
Real World Use Case: HERE Technologies Creates Data to Fine-Tune Maps Faster Than Ever
With a goal of creating three-dimensional maps that are accurate down to a few centimeters, HERE has remained an innovator in the space since the mid-’80s, giving hundreds of businesses and organizations detailed, precise and actionable location data and insights. HERE has an ambitious goal of annotating tens of thousands of kilometers of driven roads for the ground truth data that powers their sign-detection models. Parsing videos into images for that goal, however, is simply untenable. Our Machine Learning assisted Video Object Tracking solution presented a perfect solution to this lofty ambition. That’s because it combines human intelligence with machine learning to drastically increase the speed of video annotation. (Read the full case study here)
What Appen Can Do For You
At Appen, our data annotation experience spans over 20 years. By combining our human-assisted approach with machine-learning assistance, we give you the high-quality training data you need. Our text annotation, image annotation, audio annotation, and video annotation will give you the confidence to deploy your AI and ML models at scale. Whatever your data annotation needs may be, our platform and managed service team are standing by to assist you in both deploying and maintaining your AI and ML projects.
Contact us today
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Data Annotation in 2023: Why it matters & Top 8 Best Practices
What is data annotation?
Why does data annotation matter, what are the different types of data annotation, what is the difference between data annotation and data labeling, what are the main challenges of data annotation, what are the best practices for data annotation.
Annotated data is an integral part of various machine learning and artificial intelligence (AI) applications. It is also one of the most time-consuming and labor-intensive parts of AI/ML projects. Data annotation is one of the top limitations of AI implementation for organizations.
Tech leaders and developers need to focus on improving data annotation for their data-hungry digital solutions. To remedy that, we recommend getting an in-depth understanding of data annotation.
Our research covers the following:
- Why it matters?
- What its techniques/types are?
- What are some key challenges of annotating data?
- What are some best practices for data annotation?
Data annotation is the process of labeling data with relevant tags to make it easier for computers to understand and interpret. This data can be in the form of images, text, audio, or video, and data annotators need to label it as accurately as possible. Data annotation can be done manually by a human or automatically using advanced machine learning algorithms and tools. To learn more about automated data annotation/labeling, check out this quick read .
For supervised machine learning, labeled datasets are crucial because ML models need to understand input patterns to process them and produce accurate results. Supervised ML models (see figure 1) train and learn from correctly annotated data and solve problems such as:
- Classification: Assigning test data into specific categories. For instance, predicting whether a patient has a disease and assigning their health data to “disease” or “no disease” categories is a classification problem.
- Regression: Establishing a relationship between dependent and independent variables. Estimating the relationship between the budget for advertising and the sales of a product is an example of a regression problem.
Figure 1: Supervised Learning Example
For example, training machine learning models of self-driving cars involve annotated video data. Individual objects in videos are annotated, which allows machines to predict the movements of objects.
Other terms to describe data annotation include data labeling, data tagging, data classification, or machine learning training data generation.
Annotated data is the lifeblood of supervised learning models since the performance and accuracy of such models depend on the quality and quantity of annotated data. Machines can not see images and videos as we do. Data annotation makes the different data types machine-readable. Annotated data matters because:
- Machine learning models have a wide variety of critical applications (e.g., healthcare) where erroneous AI/ML models can be dangerous
- Finding high-quality annotated data is one of the primary challenges of building accurate machine-learning models
Please see our data labeling article for more on why data annotation/data labeling matters and how to choose the right data annotation partner.
Data collection is a prerequisite of data annotation, and it must be done right to ensure the overall quality of the dataset. Clickworker offers both data collection and annotation services through a crowdsourcing platform. Their global workforce of over 4 million registered data collectors offers diverse and scalable datasets and image annotation services.
For more, check out our:
- Article on data collection.
- Data-driven list of data collection/harvesting services.
Different data annotation techniques can be used depending on the machine learning application. Some of the most common types are:
1. Text annotation
Text annotation trains machines to better understand the text. For example, chatbots can identify users’ requests with the keywords taught to the machine and offer solutions. If annotations are inaccurate, the machine is unlikely to provide a useful solution. Better text annotations provide a better customer experience. During the data annotation process, with text annotation, some specific keywords, sentences, etc., are assigned to data points. Comprehensive text annotations are crucial for accurate machine training. Some types of text annotation are:
1.1. Semantic annotation
Semantic annotation (see figure 2) is the process of tagging text documents. By tagging documents with relevant concepts, semantic annotation makes unstructured content easier to find. Computers can interpret and read the relationship between a specific part of metadata and a resource described by semantic annotation.
Figure 2: Semantic Annotation Example
1.2. Intent annotation
For example, the sentence “I want to chat with David” indicates a request. Intent annotation analyzes the needs behind such texts and categorizes them, such as requests and approvals.
1.3. Sentiment annotation
Sentiment annotation (see Figure 3) tags the emotions within the text and helps machines recognize human emotions through words. Machine learning models are trained with sentiment annotation data to find the true emotions within the text. For example, by reading the comments left by customers about the products, ML models understand the attitude and emotion behind the text and then make the relevant labeling such as positive, negative, or neutral.
Figure 3: Sentiment Annotation Example
2. Text categorization
Text categorization assigns categories to the sentences in the document or the whole paragraph in accordance with the subject. Users can easily find the information they are looking for on the website.
3. Image annotation
Image annotation is the process of labeling images (see figure 4) to train an AI or ML model. For example, a machine learning model gains a high level of comprehension like a human with tagged digital images and can interpret the images it sees. With data annotation, objects in any image are labeled. Depending on the use case, the number of labels on the image may increase. There are four fundamental types of image annotation:
3.1. Image classification
First, the machine trained with annotated images then determines what an image represents with the predefined annotated images.
3.2. Object recognition/detection
Object recognition/detection is a further version of image classification. It is the correct description of the numbers and exact positions of entities in the image. While a label is assigned to the entire image in image classification, object recognition labels entities separately. For example, with image classification, the image is labeled as day or night. Object recognition individually tags various entities in an image, such as a bicycle, tree, or table.
3.3. Segmentation
Segmentation is a more advanced form of image annotation. In order to analyze the image more easily, it divides the image into multiple segments, and these parts are called image objects. There are three types of image segmentation:
- Semantic segmentation: Label similar objects in the image according to their properties, such as their size and location.
- Instance segmentation: Each entity in the image can be labeled. It defines the properties of entities such as position and number.
- Panoptic segmentation: Both semantic and instance segmentations are used by combining.
Figure 4: Image annotation example
4. Video annotation
Video annotation is the process of teaching computers to recognize objects from videos. Image and video annotation are types of data annotation methods that are performed to train computer vision (CV) systems , which is a subfield of artificial intelligence (AI).
Video annotation for a retail store surveillance system:
Click here to learn more about video annotation.
5. Audio annotation
Audio annotation is a type of data annotation that involves classifying components in audio data. Like all other types of annotation (such as image and text annotation), audio annotation requires manual labeling and specialized software. Solutions based on natural language processing (NLP) rely on audio annotation, and as their market grows (projected to grow 14 times between 2017 and 2025), the demand and importance of quality audio annotation will grow as well.
Audio annotation can be done through software that allows data annotators to label audio data with relevant words or phrases. For example, they may be asked to label a sound of a person coughing as “cough.”
Audio annotation can be:
- In-house, completed by that company’s employees.
- Outsourced (i.e., done by a third-party company.)
- Crowdsourced . Crowdsourced data annotation involves using a large network of data annotators to label data through an online platform.
Learn more about audio annotation.
6. Industry-specific data annotation
Each industry uses data annotation differently. Some industries use one type of annotation, and others use a combination to annotate their data. This section highlights some of the industry-specific types of data annotation.
- Medical data annotation: Medical data annotation is used to annotate data such as medical images (MRI scans), EMRs, and clinical notes, etc. This type of data annotation helps develop computer vision-enabled systems for disease diagnosis and automated medical data analysis.
- Retail data annotation: Retail data annotation is used to annotate retail data such as product images, customer data, and sentiment data . This type of annotation helps create and train accurate AI/ML models to determine the sentiment of customers, product recommendations , etc.
- Finance data annotation: Finance data annotation is used to annotate data such as financial documents, transactional data, etc. This type of annotation helps develop AI/ML systems, such as fraud and compliance issues detection systems.
- Automotive data annotation: This industry-specific annotation is used to annotate data from autonomous vehicles, such as data from cameras and lidar sensors. This annotation type helps develop models that can detect objects in the environment and other data points for autonomous vehicle systems.
- Industrial data annotation: Industrial data annotation is used to annotate data from industrial applications, such as manufacturing images, maintenance data, safety data, quality control, etc. This type of data annotation helps create models that can detect anomalies in production processes and ensure worker safety.
Data annotation and data labeling mean the same thing. You will come across articles that try to explain them in different ways and make up a difference. For example, some sources claim that data labeling is a subset of data annotation where data elements are assigned labels according to predefined rules or criteria. However, based on our discussions with vendors in this space and with data annotation users, we do not see major differences between these concepts.
- Cost of annotating data: Data annotation can be done either manually or automatically. However, manually annotating data requires a lot of effort, and you also need to maintain the quality of the data.
- Accuracy of annotation : Human errors can lead to poor data quality, and these have a direct impact on the prediction of AI/ML models. Gartner’s study highlights that poor data quality costs companies 15% of their revenue.
- Start with the correct data structure: Focus on creating data labels that are specific enough to be useful but still general enough to capture all possible variations in data sets.
- Prepare detailed and easy-to-read instructions: Develop data annotation guidelines and best practices to ensure data consistency and accuracy across different data annotators.
- Optimize the amount of annotation work: Annotation is costlier and cheaper alternatives need to be examined. You can work with a data collection service that offers pre-labeled datasets.
- Collect data if necessary: If you don’t annotate enough data for machine learning models, their quality can suffer. You can work with data collection companies to collect more data.
- Leverage outsourcing or crowdsourcing if data annotation requirements become too large and time-consuming for internal resources.
- Support humans with machines: Use a combination of machine learning algorithms (data annotation software) with a human-in-the-loop approach to help humans focus on the hardest cases and increase the diversity of the training data set. Labeling data that the machine learning model can correctly process has limited value.
- Regularly test your data annotations for quality assurance purposes.
- Have multiple data annotators review each other’s work for accuracy and consistency in labeling datasets.
- Stay compliant: Carefully consider privacy and ethical issues when annotating sensitive data sets, such as images containing people or health records. Lack of compliance with local rules can damage your company’s reputation.
By following these data annotation best practices, you can ensure that your data sets are accurately labeled and accessible to data scientists and fuel your data-hungry projects.
You can also check our data annotation services and video annotation tools lists to choose the fit that best suits your annotation needs.
For more in-depth knowledge of data collection, feel free to download our comprehensive whitepaper:
If you have questions about data annotation, we would like to help:
Gülbahar is an AIMultiple industry analyst focused on web data collections and applications of web data.
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Image annotator assistant.
The Laboratory for Knowledge Discovery in Database (KDD) in the Computer Science (CS) Department at Kansas State University in Manhattan, Kansas, USA are seeking undergraduate/graduate image annotators for their research lab.
Job Description
Data annotation is an essential process in training automated image processing techniques, such as deep learning, and requires meticulous attention to detail. We seek individuals with an interest in computer science and machine learning that possess a keen eye for detail. As an image annotator, you will assist with the annotation of data (e.g., images and videos) to establish annotated datasets for research usage, and work in close collaboration with research teams and affiliates.
Responsibilities
- Correctly identify, analyze, and collect relevant data from images and videos as dictated by research staff
- Gain an in-depth knowledge (through on-the-job training) of using both commercial and custom annotation software tools
- Provide feedback to further develop the annotation of datasets and workflows
- Communicate efficiently with the director, team members, researchers, and affiliates at KDD
Necessary Qualifications
- Demonstrated proficiency with computers
- Ability to perform repetitive tasks while paying close attention to detail
- Ability to understand and apply guidelines for data entry
- Ability to work independently yet assimilate well into a team
- Good organizational skills
- Experience working with databases and/or interacting with software developers
Compensation
Compensation starts at $10 per hour with increases after 1-2 semesters, given satisfactory performance. 10+ hours/week as available and needed.
If interested in the position, please send an e-mail documenting:
- Your experience, academic background, and interests
- Courses taken; Overall PA
- Names, e-mail addresses, and phone numbers of 1-3 references
For more information about the KDD lab, please visit the KDD lab wiki: http://www.kddresearch.org and email the lab director William H. Hsu ( http://www.cs.ksu.edu/~bhsu ) with any questions at [email protected]
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What is Data Annotation?
A short definition of data annotation.
Data annotation is simply the process of labeling information so that machines can use it. It is especially useful for supervised machine learning (ML), where the system relies on labeled datasets to process, understand, and learn from input patterns to arrive at desired outputs.
In ML, data annotation occurs before the information gets fed to a system. The process can be likened to using flashcards to teach children. A flashcard with the picture of an apple and the word “apple” would tell the children how an apple looks and how the word is spelled. In that example, the word “apple” is the label.
Other interesting terms…
- What is Machine Learning (ML)?
- What is Supervised Learning?
Read More about “Data Annotation”
Data annotation is an integral part of supervised ML. Without it, machines can’t correctly analyze inputs to give the desired outputs. In this section we will cover the different types of data annotation, and several important use cases. You can also check Data Annotation Guide: Everything a Beginner Needs to Know for more information about data annotation.
Types of Data Annotation in ML
Data can be annotated in various ways for a machine’s use, including:
1. Semantic Annotation
This method involves labeling different concepts with text like “things,” “people,” and “names.” Semantic annotation is used to train chatbots and improve the relevance of search engine results. Watch this video for more information.
2. Image and Video Annotation
Labeling images and videos allow machines to understand pictures and video content. Often, developers use bounding boxes to tell computers what to focus on so they can identify specific objects. Image and video annotation is commonly applied to autonomous vehicles and e-commerce product listing.
3. Text Classification or Categorization
This method refers to the process of extracting generic tags from unstructured text. The generic tags come from a set of predefined categories. Text classification or categorization helps users easily search for information and navigate within a website or an application.
Data Annotation Use Cases
Data annotation is useful in:
1. Improving the Quality of Search Engine Results for Multiple User Types
Search engines need to provide users with comprehensive information. Their algorithms must process high volumes of labeled datasets to give the right answer to do that. Take, for example, Microsoft’s Bing. Since it caters to multiple markets, the vendor needs to make sure that the results the search engine would provide would match the user’s culture, line of business, and so on.
2. Refining Local Search Evaluation
While search engines cater to a global audience, vendors also have to make sure that they give users localized results. Data annotators can help with that by labeling information, images, and other content according to geolocation.
3. Enhancing Social Media Content Relevance
Like search engines, social media platforms also need to provide customized content recommendations to users. Data annotation can help developers classify and categorize content for relevance. An example would be categorizing which content a user is likely to consume or appreciate based on his/her viewing habits and which he/she would find relevant based on where he/she lives or works.
Data annotation is time-consuming and tedious. Thankfully, artificial intelligence (AI) systems are now available to automate the process.
Key Takeaways
- Data annotation is the process of labeling data sets so ML systems can process them accurately.
- ML systems can’t analyze data properly if data annotation is incomplete or erroneous.
- There are several ways to annotate data sets, depending on specific use cases.
- Examples of data annotation methods include semantic, text classification, and image and video annotation.
- Text classification is one of the most common data annotation techniques we encounter, such as putting tags on blog posts to group them by topic.
- Image and video annotation helps self-driving cars recognize objects and people along the road.
- Semantic annotation helps browsers understand what people are typing into search boxes.
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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.
Sentiment Tagging:
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.
Semantic Annotation:
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 .
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5 Questions To Ask Before Getting Started With Data Annotation
Accelerating ai with data annotation.
We’re still a long way from realizing the full potential of artificial intelligence. Sorry to burst your bubble, but self-driving cars taking over the roads and robot doctors are closer to science-fiction than reality. Despite the hype around these AI-powered initiatives, the harsh truth is we still do not have enough data to speed the advancement of many of these types of AI projects. And while everyone understands that AI requires vast amounts of “big data” to continually learn and identify patterns that humans can’t, it’s really about getting “smart data” to train machine learning models. After all, artificial intelligence is only as smart as the data its fed.
While it’s not always easy to turn raw data into smart data, there is one process that helps add vital bits of information to raw data – providing structure to data that is otherwise just noise to a supervised learning algorithm – data annotation.
What is Data Annotation?
Data annotation (commonly referred to as data labeling) plays a crucial role in ensuring your AI and machine learning projects are trained with the right information to learn from. Data annotation and labeling provides the initial setup for supplying a machine learning model with what it needs to understand and discriminate against various inputs to come up with accurate outputs. By frequently feeding tagged and annotated datasets through an algorithm, you’re able to establish a model that can begin getting smarter over time. The more annotated data you use to train the model, the smarter it becomes. But you can’t do it without a little help. Producing the necessary annotation from any asset at scale is a challenge for many – mainly due to the complexity involved with annotation. Getting the most accurate labels demands time and expertise.
Humans are needed to identify and annotate specific data so machines can learn to identify and classify information. Without these labels – tagged and validated by human assistance – a machine learning algorithm will have a difficult time computing the necessary attributes. When it comes to annotation, machines can’t function without humans-in-the-loop.
For example, a court may issue a decision containing mostly unstructured data. Making any sense of this information demands the expertise of legal professionals to review and provide structure as well as context to the information, such as tagging specific clauses and citing other cases relevant to the judgement being scrutinized. This process of extraction and tagging helps provide a machine learning algorithm with bits of information it could not acquire on its own.
Ultimately, artificial intelligence can’t succeed without access to the right data. Feeding it the right information with a learnable ‘signal’ consistently added at a massive scale is going to drive constant improvement over time. That’s the power of data annotation. However, before you begin with any data annotation project, it’s important to consider the following questions.
What do you need to annotate? There are many different types of annotations, depending on what kind of form the data is in. It can range from image and video annotation, text categorization, semantic annotation, and content categorization. What is most important to achieving your particular business goals? Will one form of data help accelerate a particular project more than another? Determine what you will need to be successful first and foremost.
Is your annotation accurately representative of a particular domain? Before you start labeling data, you should understand the domain vocabulary, format and category of the data you intend to use – also known as building an ontology. Ontologies play a critical role in machine learning. According to the Wikipedia definition, ontologies are “formal naming and definition of the types, properties, and interrelationships of the entities that really or fundamentally exist for a particular domain of discourse.” In other words, ontologies give meaning to things. Think of this as teaching your AI to communicate using a common language. It is critical to identify the problem statement and understand how AI can interpret data to semantically solve a certain use case.
How much data do you need for your ML/AI project? The likely answer is as much data as possible, but in some instances certain benchmarks can be established based on the specific need (e.g. the past 10 years of SEC regulatory data). This is likely determined by having a domain expert handle the annotations and continually evaluate accuracy that will help create the ground truth data that will be used to train your algorithm.
Should you outsource or annotate in-house? According to research from Cognilytica , companies spend five times as much on internal data labeling than they do with 3 rd parties. Not only is this costly, it is time intensive; taking valuable time away from your team when they could be focusing their talents elsewhere. What’s more, building the necessary annotation tools often require more work than some ML projects. But for many companies, security is an issue, so there is often hesitation to release data. But many companies have privacy and security procedures in place to address these concerns.
Do you need your annotators to be subject matter experts? Depending on the complexity of the data you are annotating, it is vital to have the right expert handle annotations. While several companies use the crowd for basic annotations, more complex data requires specialized skills to ensure accuracy. For example, being able to interpret complex legal obligations and agreements from ISDA contracts require legal specialists that can identify and label the most appropriate information. The same goes for other fields like science and medicine where deep understanding and fluency of the content cannot be taken for granted. If there are even slight errors in the data or in the training sets used to create predictive models, the consequences can be potentially catastrophic.
Mike Goldberg
Michael Goldberg enjoys learning and reading about cutting-edge technology, data and AI. When afforded the opportunity, he's more than happy to share his thoughts on what it means for us - professionally and personally.
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