What Is Data Labeling?

In this article, you’ll learn about the importance of data labeling and what the process looks like. You’ll also review some data labeling use cases and discover techniques to enhance efficiency.

The Crucial Role of Data Labeling in ML

Data labeling is the process of tagging or annotating data, providing the ground truth that supervised learning models need to learn and make predictions. By assigning accurate labels to training data, you enable models to identify patterns, understand relationships, and predict outcomes accurately.

In essence, data labeling teaches models to identify different things. Without properly labeled data, these models would struggle to distinguish between different entities. In ML, especially supervised learning, data labeling is important because it directly impacts how well a model learns and how accurate its predictions are when applied to new, unseen data.

Types of Data Labeling

Because ML involves a large quantity of data to train the models and, more often than not, this data comes from various sources (including books, stock images, and public audio/video records), labeling it can involve several different processes.

Natural Language Processing

Natural language processing (NLP) focuses on processing data that contains human language, such as written text or recorded speech. This ML-based technique helps computers make sense of and understand such data. NLP can also automate data labeling using techniques like named entity recognition (NER) to identify entities (eg names, dates), text classification to categorize data, and sentiment analysis to label emotions or opinions:

NLP makes use of pretrained ML models to predict and tag similar patterns in new data, which can greatly reduce manual work.

Computer Vision

Computer vision is a subdomain of artificial intelligence (AI) that enables computers to interpret image data objectively. This means that instead of just treating images like a file with a specific extension, computers can, with the help of computer vision, identify entities and places (even human actions) in the images. They can segment parts of images based on instructions and can also help classify images based on specified criteria (ie flag every image that has an apple in it).

Pretrained ML models assist in automated data labeling by predicting labels for new, similar data. This speeds up the labeling process and improves the consistency of large-scale datasets that are used for training ML models.

Audio Processing

Audio processing refers to analyzing (and optionally, modifying) sound files to extract useful information, such as speech, music, or environmental sounds. Multiple techniques like noise reduction, feature extraction (eg pitch, frequency), and converting audio to text through speech recognition, are used to gather insights from audio files.

Audio processing can streamline data labeling by automatically transcribing speech to text, identifying speakers, detecting events (eg gunshots, alarms), and classifying sounds. This is particularly useful when annotating large audio datasets, reducing the need to manually sift through hours or even days of raw audio data to flag events, speakers, and other points of interest.

Large Language Models

The newest item on this list is a large language model (LLM). LLM is a type of AI model trained on vast amounts of data to understand and generate human-like language. LLMs can perform a wide range of natural language tasks, such as translation, summarization, text completion, and question answering.

LLMs can generate labels for text data (eg sentiment, topic categorization), suggest tags based on patterns in the data, and even refine or correct manual annotations. Moreover, many LLMs can process image inputs and help you label objects in images, too.

Apart from labeling data, LLMs can quickly gather data from the internet to train your ML models. AI web scraping, which hitches up your regular web scraping setup with an LLM to quickly make sense of website structures and available data, can help you sift through large amounts of data collected from the web, make sense of this data, and even label it on the fly. AI web scraping can also look at the Document Object Model (DOM) structure of a website to gather data and take screenshots of a website as it is displayed to the users. AI web scraping tools can then process these screenshots to gather data. If you want to learn more about AI web scraping, check out this blog post, “How to Use AI for Web Scraping”.

Data Labeling Approaches

Data can come in all kinds of formats, and there are methods you need to follow to label data for each of these formats. The approach to labeling data varies across companies and projects. Here are some of the most common ways teams approach data labeling tasks:

Internal Labeling

When teams label their data in-house, it’s referred to as internal labeling. Internal labeling is typically used when accuracy, control, and domain expertise are required.

If you’re looking for quality and consistency, this method is ideal. With a dedicated team of professionals, the data labels are highly specific to the domain of the data set and the project, which further helps with the accuracy of the trained models. Additionally, because the data labels are created internally, the data remains private and secure.

However, a major downside to this approach is that it’s not scalable. The size of internal teams working on such tasks is usually limited, so getting a useful amount of data labeled is a time-consuming and expensive task.

Synthetic Labeling

Synthetic labeling uses metadata; it refers to generating labeled data from preexisting datasets using ML.

The main advantage of synthetic labeling is its scalability and cost-effectiveness. By generating data artificially, you can quickly create large datasets without the time and expense associated with collecting real-world examples. Additionally, synthetic data allows for the simulation of rare events or edge cases that might be difficult or unsafe to capture in real life.

However, the downside is that synthetic labels may not fully capture the complexities of real-world scenarios, which can impact the accuracy and performance of the models. Creating high-quality synthetic data requires expertise with ML techniques, adding complexity to an otherwise simple process. Moreover, the quality of data generated in this process greatly depends on the initial training data of the model being used.

Programmatic Labeling

Programmatic labeling refers to the use of rules, algorithms, or scripts to automate the labeling process. It’s typically used when working with large-scale datasets where manual labeling would be too time-consuming and when the data can be structured with clear, rule-based patterns, such as in-text classification or sentiment analysis.

The biggest benefit of programmatic labeling is its speed and scalability. Automated methods can process vast amounts of data much faster than human efforts, significantly reducing manual labor and enabling rapid data set expansion. This approach is particularly effective for simple, repetitive labeling tasks where consistent rules can be applied.

However, a key drawback is the lower accuracy compared to manual labeling, especially when dealing with complex or anomalous data that may not fit neatly into predefined rules. Additionally, data labeled using this method must be validated and refined frequently to ensure quality, which can still require a lot of human intervention.

Outsourcing

Outsourcing involves contracting external providers or companies to handle data labeling tasks. This approach is used when internal teams lack the capacity or when projects require large-scale labeling that needs to be completed quickly and efficiently.

Outsourcing is cost-effective when it comes to handling large volumes of data. By outsourcing to external entities, teams can scale their labeling efforts without investing heavily in building and training in-house professionals. Additionally, it frees up internal resources to focus on core tasks and project development.

However, the quality of outsourced labeling can vary as external teams rarely have the same level of domain expertise or understanding of project-specific requirements. There are also potential risks related to data privacy and security as sensitive information needs to be shared with third parties.

Crowdsourcing

Crowdsourcing involves distributing data labeling tasks to a large, diverse group of non-expert workers through platforms like Amazon Mechanical Turk. It’s typically used for tasks that can be broken down into simple, high-volume units, such as image tagging or basic text classification.

The main advantage of crowdsourcing is its scalability and speed. By using a large, distributed workforce, teams can quickly label large datasets at a relatively low cost, making it an efficient option for straightforward labeling tasks that don’t require specialized expertise.

However, the quality and accuracy of crowdsourced labels can be inconsistent as the workers may lack domain-specific knowledge. Ensuring uniformity and precision across labels can be challenging, and quality control measures, such as redundancy and validation, are often needed. Despite its cost-effectiveness, crowdsourcing may not be suitable for complex labeling tasks requiring expertise or in scenarios where data privacy is critical.

Using Trusted Datasets

While manual, programmatic, and crowdsourced methods provide various approaches to labeling, access to prelabeled, high-quality datasets can significantly enhance scalability. Trusted datasets, like those offered by Bright Data, provide a ready-to-use solution for large-scale data collection, ensuring consistency and accuracy while reducing the time and effort required for labeling.

When you use trusted datasets in your workflow, you can accelerate model development, focus on refining algorithms, and maintain high standards of data quality, ultimately optimizing the labeling process for more effective ML results.

Challenges in Data Labeling

Regardless of which method and approach you choose, you will encounter challenges when working on data labeling tasks.

Imbalanced Datasets

One of the most common issues is imbalanced datasets, where certain classes or categories have significantly fewer examples than others. This can lead to biased models that perform well on majority classes but poorly on minority ones. Ensuring sufficient representation of all categories requires either collecting more data or generating synthetic samples, both of which can be time-consuming and resource-intensive.

Noisy Labels

Noisy labels occur when data is labeled incorrectly, whether due to manual error, ambiguities in the labeling guidelines, or inconsistencies in crowdsourced work. Noisy labels can significantly degrade model performance as the model may learn incorrect patterns or associations. You can solve this with techniques like label validation, redundancy, and refining of labeling criteria, all of which can increase the time and cost of the labeling process.

Scaling Issues

As the volume of data required for training models grows, you need to be able to scale the labeling process. Traditional manual labeling methods are not always practical, and even automated methods like programmatic or outsourced labeling come with limitations, such as reduced accuracy or data privacy concerns. Achieving both scale and quality in labeling requires balancing automation with human oversight, which can be complex to manage.

Dynamic Data

In most real-world applications, data is constantly changing/evolving, making it necessary to continuously update labeled datasets. This is especially relevant in domains like real-time monitoring or autonomous driving. Keeping datasets up-to-date and relevant requires implementing efficient pipelines for ongoing labeling and validation, which adds another layer of complexity to the labeling process.

Data Labeling Best Practices

There are a few techniques you should keep in mind to help you efficiently label your data with high-quality labels.

Label Auditing

The first and most obvious best practice is label auditing. This involves examining a subset of labeled samples to identify errors, inconsistencies, or ambiguities in the labeling process. When you catch mistakes early, teams can refine guidelines and provide targeted feedback, ensuring the entire data set remains accurate.

Transfer Learning

Similar to programmatic labeling but with a more human touch, transfer learning has teams using pretrained models to assist in labeling new datasets. The models can predict and suggest labels based on their prior knowledge, making it faster and more efficient to label large datasets.

Active Learning

Active learning focuses on selecting the most informative or uncertain samples for human labeling. By prioritizing these samples, teams can improve the efficiency of their labeling efforts and apply human expertise where it adds the most value. This approach helps refine models faster while minimizing the overall labeling workload.

Consensus

Consensus methods can be used in crowdsourced or outsourced settings to improve label accuracy. In such methods, multiple labels annotate the same sample, and the final label is determined based on agreement among the multiple labels. There can be many customizations and ways to determine the consensus, such as relying on a majoritarian voting system or pruning out annotation submissions based on preset rules.

Data Labeling Use Cases

Now that you know how to label data, let’s take a look at some of the most common ML use cases:

• Sentiment analysis: In sentiment analysis, data labeling helps by tagging text with sentiments like positive, negative, or neutral. By providing accurately labeled text samples, ML models can learn to understand and predict the sentiment of new, unseen texts. This is used in social media monitoring, customer feedback analysis, and market research to gauge public opinion or customer satisfaction.
• NER: For NER tasks, data labeling helps identify and tag entities, such as names, dates, locations, or organizations, within text. Labeled data helps train models to automatically extract these entities, which is essential for applications like chatbots, information retrieval systems, and document automation.
• Image classification: Image classification relies on labeled images that identify objects, scenes, or features. Labeling images helps models learn to recognize and classify new images accurately, which is useful for applications in autonomous vehicles, medical imaging, and facial recognition systems.
• Text classification: In text classification, labeled data assigns categories or topics to different text samples. This enables models to categorize new documents, emails, or messages efficiently. Common applications include spam detection, content moderation, and document organization.
• Fraud detection: For fraud detection, labeling involves identifying patterns and anomalies in transaction data. By labeling fraudulent and legitimate instances, models can be trained to detect unusual behavior, enhancing the accuracy of systems used in finance and e-commerce to protect against fraud.

Use Bright Data for Data Labeling

As stated previously, Bright Data offers high-quality datasets that significantly improve the accuracy and efficiency of the data labeling process. Through its extensive data collection capabilities, Bright Data provides AI teams with up-to-date, vast, diverse, and accurately labeled datasets, which are essential for training models.

Bright Data datasets are customized to various domains, ensuring that models receive precise, domain-specific information for optimal performance. They also can help you reduce labeling errors and achieve higher levels of model performance and efficiency. You can use these datasets as they are in your primary ML training exercises, or you can use them to assist with your synthetic or programmatic labeling efforts.

Bright Data datasets also help support scaling your labeling processes. With access to large-scale, structured datasets across various domains like social media, real estate, and e-commerce, AI teams can accelerate the labeling process, reducing the need for manual efforts and speeding up development cycles. This scalability allows businesses to handle massive volumes of data, which is essential for building AI solutions.

Conclusion

Data labeling is an important step in the development of ML models, providing the structured information needed for algorithms to learn and make accurate predictions. This article discussed various techniques and approaches to data labeling, along with its key use cases, like sentiment analysis (where text is labeled with emotions) and fraud detection (where anomalies are tagged to identify suspicious activities).

See how Bright Data can help you with your projects by providing data for AI in the form of ready-to-use datasets. Sign up now and start your data journey with a free trial!