Human Annotated Data – All You Need to Know About It
Digitalization is rapidly bringing in new technology that is making our lives easier. Businesses now have access to tools and technologies that help them streamline their process. Today, companies are looking to leverage AI (Artificial Intelligence) and ML (Machine Learning) capabilities to get a competitive edge over others.
Machine Learning is now becoming a vital element of business operations. The AI and ML models’ performance depends on the data quality they work with. Therefore, it shows how important it is to collect suitable datasets for Machine Learning and what methods we should use to collect them.
In most cases, the data we are working with already has high-quality labels. For instance, if you are projecting the stock prices from the previous values, the price will act as both the input feature and the target label.
But, that is not the case all of the time since the data labels might not be of high quality. Some labels, such as user-added tags and categories, can be opinionated or subjective. In other instances, the data might not have any labels, such as object detection.
That is where data annotation comes into play since it helps you acquire labels and enhance their quality. The process of data annotation entails labeling or relabeling the data using annotation tools and algorithms. Consequently, it helps with a lot of things, such as:
- Enhancing model performance
- Improving data quality
- Making model training possible
So, what is human-annotated data? Why is it so important? And what are the advantages of using it? We’ll discuss everything in this article to give you a comprehensive idea about data annotation.
What is Data Annotation?
Most of the data that is available today is unstructured. It is not defined properly or correctly. When building an AI model, it is necessary to feed the correct information into the algorithm to analyze and deliver the desired results and inferences.
This process can only occur if the algorithm understands the data you feed. It will allow the algorithm to classify the data accordingly.
The process of getting data in a position easily understandable for the ML algorithms is known as data annotation. It entails attributing, tagging, or labeling data accordingly so the ML and AI projects can understand it.
In a nutshell, data labeling and annotation is tagging the appropriate details/information in the dataset. Doing so will allow the machines to understand and use the data set accordingly. Also, the data set can be in any form, such as images, videos, audio, text, etc.
Role of Data Annotation in Machine Learning and Artificial Intelligence
Labeling components in data will allow the ML models to comprehend precisely the information they will process. The models will also store the previous information to automatically process the new detail built on existing knowledge to take timely decisions.
In addition to comprehending data, the data annotation process also helps AI and ML models to know if the data set they receive is a visual, audio, image, video, text, or a combination of formats. Next, the model will classify the data and perform task execution according to your assigned features and parameters.
Data annotation is unavoidable since AI and ML models and projects need consistent training to provide more effective and efficient outcomes. This process becomes essential during supervised learning since the more annotated data you feed to the model, the more quickly it will start to train itself without help.
Let’s take about self-driving cars as an example; the automobile relies on data coming from different tech elements, such as:
- Other tech elements
- NLP (Natural Language Processing)
- Computer vision
The algorithms in these tech elements use data annotation to allow the vehicle to take precise driving decisions at every point. If not, the AI and ML model won’t understand whether the approaching obstacle is a person, animal, or another vehicle.
Therefore, without data annotation, the AI model’s results would provide unfavorable outcomes. The implementation of the data annotation will allow you to train your AI models precisely. As a result, you will get a complete model that will give you desired results whether you are deploying the model for speech recognition, chatbots, or any other process.
Raw AI training data sets as well as human annotated data like images can be can be obtained easily and quickly via clickworker.More about Image Annotation Services
Types of Data Annotations
There are different types of data annotation that you need to know about for your ML projects. Every data type has a separate labeling procedure, so below are a few examples of the most common types of data annotations.
Video annotation uses methods like the bounding boxes to find the motion frame-by-frame. It gives you data vital for the AI and ML models that conduct object location and tracking. Video annotation enables easy implementation of various concepts like searching objects, motion blur, etc., in the systems.
Text annotation is the technique of designating the text in a particular document in different categories depending on the topic and context. From a social media mention to customer reviews about a product, this text or the material can be about anything.
Texts can give you a clear and better idea about the intentions, and it is easy to get practical and valuable information from it through text annotation. However, an important thing to note is that the process of text annotation can be a bit complex and has various phases since the ML models are unaware of concepts and emotions.
The image Annotation allows ML models to see the annotated area as a different item. When training such models, you need to use captions, alt text, and keywords to describe the images.
This way, the algorithm can easily find and understand the images. Image annotation usually entails using AI-based applications for bounding boxes and semantic segmentation.
Audio Annotation needs to identify various parameters in the audio. It is done with the help of tagging that uses different techniques like:
- Acoustic scene classification
- Music tagging
Apart from the verbal cues, you can also annotate instances like silence and breadths.
Semantic annotation refers to adding tags to various concepts, such as people, organization names, places, etc., in the document. This will assist ML models in dividing the new concepts in the future text into appropriate categories.
This annotation is essential for AI and ML training to enhance Chabot’s capabilities and improve search relevance. The semantic annotation usually includes tagging key phrases and the correct identification parameter.
Why is Data Annotation Necessary?
Today, computers have the capability of delivering results that are not only accurate but also precise and timely. So, what is the way Machine Learning can develop the same capabilities and deliver results with efficiency?
The answer to the question is through data annotation. When the ML modules go through the development phase, they will intake massive volumes of AI training data. As a result, it would help them make better decisions and discover the objects or elements.
If we eliminate the process of data annotation from the machine learning model, each image or visual will be the same for it. They wouldn’t have information or understanding about any object in front of them. Therefore, data annotation is a necessary element for the systems to:
- Understand recognition models
- Train computer vision and speech
- Help modules identify elements
- Deliver accurate results
Any model driven by ML or AI capabilities needs to utilize the data annotation processes to ensure that their decisions are accurate and relevant.
Human Annotated Data in Machine Learning
Another essential thing today in the data labeling and annotation process is the involvement of human beings. Human-annotated data refers to the sources of data annotations that come through humans.
Humans can learn, recognize, and understand things that ML models can’t comprehend. Below are a few things that humans might be able to identify and understand better than the AI and ML models within specific contexts:
- Understanding whether a data point is worthy and beneficial within the context of a business problem
- Uncertainty, vague ideas, and irregular varieties;
- Purpose and subjectivity
- Contexts relevant to the issue that the organization is facing
In addition to these points, compliance with specific regulations and points might also need the help of a human in the ML workflow. The step you’ll need help from human or automatic annotation will vary from situation to situation.
Most companies use semi-automated annotation strategies that mix the automated ML process and manual labeling approaches.
How is Data Annotation Different from Data Labeling?
Most people confuse data annotation and labeling as the same thing, but that is not the case. Though both use the same style and content tagging, some slight differences set them apart.
Thus, most people use this term interchangeably when creating data sets for ML training. Here are a few critical differences that set the data annotation and labeling apart.
- Data annotation assists ML models in identifying the relevant data. On the other hand, data labeling assists them in determining patterns so they can train algorithms accordingly.
- Data annotation is integral to the ML model training and learning processes. Conversely, data labeling is about finding the relevant features and specifications in the data set.
- The process of data annotation entails using techniques to label data so the machine learning models can quickly learn about the objects. Data labeling is about including more details/metadata in different data types, such as images, videos, audio, etc. Doing so will make the training process for the ML models easy.
What Things do You Need to Keep in Mind for Data Annotation in Machine Learning?
Now that you have a clear idea about data annotation and why it’s necessary for your ML projects, the next important thing is using it properly. If you want to make the most out of the data annotation, you need to see it as a part of your ML workflow.
It will require you to come up with a mixture of software elements, algorithms, annotators, etc. Also, you need to ask two questions for your data annotation project:
- How are you going to utilize the limited resources for data annotation effectively?
- How will you assess the quality of your annotations?
You can use various techniques to address these problems. To give you a better idea, we’ll give an overview of the two most effective techniques, which are :
Active learning tells you the methods to sample the data for annotation, whereas quality assessment is about validating annotation performance. Let’s discuss them in more detail.
Active Learning: Ways to Sample Data for Annotation
Active Learning refers to choosing data samples while keeping the point of data annotation at the forefront. Before combining human annotation with ML models, deciding which elements of the data humans will annotate is necessary.
Since the resources necessary for the data annotation are scarce, you must utilize them effectively. You can choose from different types of active learning for data annotation. It can help you save time and money. Below are the three most common ones that most people use.
Diversity sampling refers to the general paradigm that attempts to discover underrepresented or unknown values in your model. It can come in handy when you have to choose from various options. It is also known as:
- Stratified Sampling
- Representative Sampling
- Anomaly or Outlier Detection
One of the key advantages of using this tool is that it can allow the model to learn about underrepresented information and details. In some cases, ML models will ignore certain information in the datasets due to their low occurrences. However, diversity sampling allows them to learn about such models.
Additionally, diversity sampling helps you avoid performance loss because of data drift. It usually happens when the AI or ML model contains too much data from the old and inaccurate sample regions.
Uncertainty sampling refers to the process of choosing unlabeled samples that are close to the decision-making capabilities of the model. The benefit of this method is that you can identify samples with a higher possibility of being wrongly classified. So, you can manually annotate them to mitigate the chances of any errors.
Lastly, random sampling is also a kind of active learning, and it is the simple one you can use. But the one challenge you might face is finding a random sample might be easy due to the distribution of the received data. Also, there are specific issues that you can catch with other methods but not with random sampling.
Quality Assessment: Validating Annotation Performance
Once you complete the sampling step, the next thing to do so is a proper QA. There is a chance the annotators can make mistakes or fail to identify any possible drawbacks. Therefore, introducing proper checkpoints to spot these mistakes is vital. Below we have covered some points to help you enhance the annotation performance:
- Have Annotators with the Right Expertise: Having experienced annotators and experts can offer you high-quality details and can do the final reviews.
- Dedicate a Team: It is better to have experienced people working on the project. It will increase the annotation accuracy and ensure everyone is on the same page regarding the relevancy.
- Diversification: Getting people from different backgrounds, abilities, and expertise will ensure no systematic bias.
We have listed the four best practices for handling the quality assurance process to make things easy. Bloomberg’s Global Data department collects these practices. Here is a table explaining the quality assessment methods, their benefits, and their drawbacks.
Sample randomly for AQ
- You can review massive quantities of annotations
- It does not need any follow-up discussions or preparation
- Won’t be able to focus on possible errors
Get the work items ready and compare them directly with the annotation “answer keys.”
- Provides you with instant feedback with quantifiable outcomes
- It only applies “objective” answer types
- It needs some preparation work
Annotation Redundancy with Targeted QA
Conduct various annotations and do a proper QA on disagreeing results
- It doesn’t require any preparation
- Pinpoints the peculiarities
- The feedback loops are quite longer
- It also has a higher annotation time
Annotation Redundancy with Debrief
Conduct various annotations and discuss the guidelines that annotators apply
- It doesn’t require any preparation.
- Can identify subjective data with a variety of possible answers
- Debrief takes quite some time
- The feedback loops are quite longer
Human Annotated Data – The Bottom Line
Data annotation allows the AI and ML models to understand whether the data they get is audio, video, image, text, visuals, or a combination of all these formats. Depending on the specifications and the set parameters, the model will categorize the data and approve the execution of relevant tasks.
The data annotation makes sure that your model is trained properly so that it can produce the best outcomes over the long term. Data annotation will provide you with a perfect model for every activity, regardless of whether you use image recognition or chatbots.
FAQs on Human Annotated Data
What are common examples of human data annotation?
Human data annotation is the process of adding metadata or other information to data by a person. Here are some common examples of human data annotation:
- Image annotation: Adding labels or tags to describe the content or context of images.
- Text annotation: Adding labels or tags to classify or extract relevant information from text.
- Video annotation: Adding labels or tags to describe the content or context of videos.
- Speech annotation: Transcribing and annotating audio data to classify or extract relevant information.
- Sentiment annotation: Adding labels or tags to indicate the sentiment or emotion expressed in text.
What is the benefit of human data annotation?
Human data annotation has several benefits, including:
- Improved machine learning performance and accuracy.
- Enhanced search and retrieval of specific pieces of information.
- Organized and structured data that is easier to understand and use.
- Improved data quality.
- Customized data that meets specific needs or goals.
Why should you let people annotate data?
There are several reasons to let people annotate data, including:
- Accuracy: People are often better at accurately annotating data than automated methods.
- Consistency: People can ensure that the annotations are consistent and follow established guidelines.
- Context: People can provide context and background information when annotating data.
- Customization: People can tailor the annotations to meet specific needs or goals.
- Human expertise: People may have specialized knowledge or expertise that can be valuable for annotating data.