In machine learning, data labeling refers to the process of tagging, annotating, classifying, moderating, transcribing, or processing raw data. Labeling data marks up your data to show your target the answer you want your machine learning model to predict. For example, you can use labeling to indicate the features included in a photo, words in an audio recording, or the location of street signs, pedestrians, and other vehicles.
Data annotation and labeling can be used interchangeably but can mean different things depending on the use case or industry. Data labeling
is helpful in computer vision, speech recognition, and natural language processing.
For a particular data model to succeed, it requires the involvement of human and machine intelligence. In this case, the human-in-the-loop (HITL)
configuration involves people in the virtuous circle of improvement. Human judgment helps in training, tuning, and testing the data model. HITL uses labels to identify and call out features present in the data.
Labeling data accurately provides ground truth for testing and iterating the models, hence developing high-performing algorithms. Therefore, it’s critical to use informative, discriminative, and independent features in your data labeling.
How Does Data Labeling Work?
To understand how data labeling works, you must familiarize yourself with the different approaches to data annotation. The style you choose depends on the complexity of your problem statement, the amount of data you want to tag, and the size of your data science team. The available time and financial resources also determine the best data annotation approach.
It is difficult to do data annotation at scale because it requires working procedures, quality assessment methodologies, specialized tools, properly selected and trained data annotators. It’s also important to consider how cost plays a role. If the annotation provider was selected based on price, it is very probable that the quality they obtain is poor. The approaches to choose from include:
- In-house data labeling- with this approach, you use experts within your organization to carry out the data labeling. It’s the best option if you have enough resources, a lot of time at your disposal, and a team with the subject matter knowledge to handle the labeling. However, keep in mind that companies that are annotating data internally often don’t have the right resources/tools for data annotation, and data annotation takes about 80% of the resources of an AI project.
- Outsourcing- the approach can span from freelancers to experienced annotators, project managers and data scientists. It’s recommended to have several data annotation providers to reduce risks. Sigma offers a free pilot option with no obligation, so you can test our quality, working procedures, and compare us against your current solution.
- Crowdsourcing- the approach requires you to sign up on a crowdsourcing platform as a requester and assign the labeling tasks to available contractors. Like outsourcing, the option delivers speedy results but can’t guarantee high-quality accuracy.
- Programmatic- instead of labeling your data manually, you can use AI data labeling. The approach uses an AI Auto-label model to mark raw and unlabeled data. The process is speedy and eliminates the need for human annotation. However, it may be prone to technical errors, hence the need to retain HITL as part of the quality assurance process.
- Synthetic labeling- the approach uses pre-existing datasets to generate new project data, enhancing quality and time efficiency. On the flip side, this approach requires a lot of computing power, making it costly.
Whichever approach you choose for your project, data labeling works in the following chronological order:
The first step to labeling data is to collect enough raw data from various sources depending on your industry. The data can include images, text, audio files, or videos. You can get the data you have accumulated over the years from your database or use publicly available datasets.
Whichever your source, the data is either corrupted, inconsistent, or unsuitable for use, hence the need to clean and process it before labeling. The most important thing is to ensure you collect diverse data to facilitate accuracy.
This is the most significant step in data labeling. Data science experts go through the data labeling it. The stage involves adding meaningful context that the model can use as ground truth.
Quality Assurance, QA
After labeling, the data needs to be high-quality, accurate, reliable, and consistent. The quality of the resulting datasets depends on the accuracy of the data labeling. Therefore, you must include continuous QA checks throughout the labeling.
When labeling data, labelers use various QA algorithms such as:
- Cronbach’s alpha test for measuring the average consistency of data items in a set
- The consensus algorithm for ensuring data reliability through agreement on a single data point among different systems
Including the QA stage in your data labeling process provides the highest quality results.
Model Training and Testing
At this stage, data labelers apply label data with correct answers to train the new model. The process involves testing the model using unlabeled data set to determine whether it will deliver the expected predictions and estimations.
Types of Data Labeling
There are different types of labeling depending on the AI domain, and they include:
Image and Video
The process involves attaching one label (single-label category) or several tags (multi-label classification) to an image based on the class of the depicted object. You can divide the image into regions that are labeled. It can assist with identifying objects, people, logos, etc with bounding boxes.
Bounding boxes are rectangular or square boxes used to show an object’s position within an image. They are the most common form of image annotation and are identified using the x and y-axis coordinates. They are primarily used in object detection and image classification with localization tasks.
Object detection involves detecting and classifying multiple objects in an image of a video frame. In addition, it helps point out the position of each object using bounding boxes. On the other hand, image classification with localization classifies images into a few predefined classes depending on the objects depicted and the drawing boxes around these objects. The method is mainly used to define the location of a single or several objects.
The data labeling process for audio involves dividing the content of an audio clip into segments and then attaching a label to each segment. The data labeling helps in speech recognition, transcription, and sentiment analysis. You can identify time frames for each individual speaking and also map their location in the audio clip.
Text data labeling involves determining the overall theme of the text to assign to a specific class. The aim is to identify the occurrence of particular words and phrases in a text data set. This type of data labeling is mainly used for sentiment analysis, topic modeling, and machine translation.
Sensor data labeling is the process of attaching a label to data collected from sensors. The data could be temperature data, humidity data, pressure data, etc. The aim is to identify the trend in the data and also find correlation between different sensor readings. You can use machine learning algorithms to detect patterns in the data.
Natural Language Processing (NLP)
NLP is a subset of artificial intelligence that enables machines to interpret human language
. It uses the power of statistics, linguistics, and machine learning
to study the structure and rules of the human language to develop an intelligent system for deriving meaning from text and speech.
The most popular ways of labeling text for NLP
Also known as text tagging, text classification assigns labels to text blocks for classification based on predefined subjects, trends, and other parameters.
The process involves detecting the language of the text by adding corresponding language labels to texts based on the language they are written in.
Topic classification involves detecting the topic conveyed in a text.
How to Get Started with Data Labeling
Regardless of the data labeling approach you choose, the following best practices
can help you achieve the best results.
Determine Workforce Options
There are different trade-offs when annotating with full-time employees, crowdsourcing, or partnering with companies specializing in data annotation. Be sure to weigh your options and determine what makes the most sense for your project.
Provide thorough annotation guidelines to your workforce that include tool and annotation instruction to describe how to work with and troubleshoot the tool. It’s important to also provide examples and illustrate labels. Lastly, be sure to share what the goal of the project is, so that your workforce has context that can provide motivation.
Maintaining organized and streamlined communication with your team scales up the labeling process. Having a tightly closed feedback loop with your team ensures that you can make any necessary changes fast.
Need Help With Machine Learning Data?
AI revolutionization is taking over the way we do things, and your business should not be left behind. Data labeling is the first step towards innovation. And now that you understand what data labeling is and how to achieve it, it’s time to make informed decisions and take your business to the next level.
To achieve high-quality data for your organization, you need an experienced data labeling team and robust tools. Sigma provides customized AI data labeling solutions to businesses. Contact us
today, and let’s discuss how we can help you.