Data labeling refers to the practice of identifying items of raw data to give them meaning so a machine learning model can use that data. Let’s suppose our raw data is a picture of animals. In that case, you’ll want to label all the different animals for the model including birds, horses and rabbits. Without proper labels, the machine learning model won’t know what different data types are in the picture.
Data labeling is an essential step before training or using any machine learning model. It is involved in many applications, such as computer vision, natural language processing (NLP) and image and speech recognition.
5 Data Labeling Methods
- Internal Labeling
- Synthetic Labeling
- Programmatic Labeling
How Does Data Labeling Work?
There are two main categories of machine learning algorithms: supervised and unsupervised.
In supervised machine learning algorithms, we need to provide the algorithm with labeled data for it to learn and then apply what it learned to new data. The more accurate the labeled data, the better the algorithm’s results. In most cases, data labeling starts with a person (often called “a labeler”) making some decisions on unlabeled data for the algorithm to learn.
Let’s say we want our algorithm to identify trees. To train the model, the labeler may first be presented with pictures and must answer “true” or “false,” indicating if the image contains a tree. The algorithm then uses these decisions to identify the picture pattern, learn what a tree is and then use that to predict whether future images have trees in them.
Data Labeling Methods
Since data labeling is essential in developing a good machine learning model, companies and developers take it very seriously. However, data labeling can be time-consuming, so some companies may outsource or automate the process using a tool or service.
We can use various approaches to label data; the decision between those approaches depends on the size of your data, the scope of the project and the time you need to finish it. One way to categorize different labeling methods is whether a human or computer is labeling. If humans are doing the labeling, it can take one of three forms.
This approach is used in large companies with many expert data scientists who can work on labeling the data. Internal labeling is more secure and accurate than outsourcing because it’s done in-house without sending the data to an external contractor or vendor. This approach protects your data from being leaked or misused if the outsourcing agent is unreliable.
This option can be the way to go for large, high-level projects that require more resources than the company can spare. That said, it requires managing a freelance workflow which can be costly and time-consuming because, in such cases, companies hire different teams to work in parallel to get the work done on time. In order to maintain the flow and quality of work, all teams need to use a similar approach when delivering the results. Otherwise, more effort is required to put the results in the same format.
In this approach, the company or the developer uses a service to label the data quickly and at a lower cost. One of the most famous crowdsourcing platforms is reCAPTCHA, which basically generates CAPTCHA and asks users to label the data. Then the program compares the results from different users and generates labeled data.
However, if we want to automate the labeling and use a computer to do it, we can use one of two methods.
In this approach, we generate synthetic data using the original data to enhance the quality of the labeling process. Though this approach leads to better results than programmatic labeling, it requires a great deal of computing power because you need more power to generate more data. This approach is a good choice if the company has access to a supercomputer or a computer that can process and generate huge amounts of data in a reasonable amount of time.
To save computing power, this approach uses a script to perform the labeling process instead of generating more data. However, programmatic labeling often requires some human annotation to guarantee the quality of the labeling.
Advantages of Data Labeling
Data labeling gives users, teams and companies a better understanding of the data and its use. Mainly, data labeling offers a way to offer more precise predictions and improve data usability.
More Precise Predictions
Accurate data labeling ensures better quality assurance within machine learning algorithms than using unlabeled data. This means your model will train on higher quality data and yield the expected output. Properly labeled data provide the ground truth (i.e., how labels reflect real-world scenarios) for testing and iterating subsequent models.
Better Data Usability
Data labeling can also improve the usability of data variables within a model. For example, you might reclassify a categorical variable as binary to make it more consumable for a model. Aggregating data can optimize the model by reducing the number of model variables or enabling the inclusion of control variables. Whether you’re using data to build a computer vision or NLP model, using high-quality data should be your top priority.
Disadvantages of Data Labeling
Data labeling is expensive, time consuming and prone to human errors.
Expensive and Time Consuming
While data labeling is critical for machine learning models, it can be costly from both a resource and time perspective. Suppose a business takes a more automated approach. In that case, engineering teams will still need to set up data pipelines before data processing. Manual labeling will almost always be expensive and time-consuming.
Prone to Human Error
These labeling approaches are also subject to human error (e.g., coding errors, manual entry errors), which can decrease data quality. Even small errors lead to inaccurate data processing and modeling. Quality assurance checks are essential to maintaining data quality.
Data Labeling Best Practices
Regardless of the labeling approach you choose for your data labeling project, there are a set of best practices to enhance the accuracy and efficiency of your data labeling process. For example, we build machine learning models using large amounts of quality training data, which is expensive and time consuming. In order to develop better training data, we can use one or more of the following methods:
- Labeler consensus helps counteract the errors and unconscious biases of individual labelers. Errors may include mislabeling or double labeling data. Moreover, one of the challenges in machine learning is when the data does not fully represent all possible potential labels, thereby leading to bias within the training data itself.
- Label auditing keeps the labels updated and ensures their accuracy. Often, when machine learning databases are built, they are updated regularly with new data that needs to be labeled before we store and use it. Auditing the data ensures new data is labeled correctly and that the old data is relabeled to remain consistent with those new labels.
- Active learning uses another machine learning approach to decide what small amount of data needs to be labeled or checked by a human labeler. In active learning, the human labeler labels a small amount of data first and then these labels are used to train a model on how to label future data.
Examples of Data Labeling Tools
There are many online tools and software packages that you can use to label data using any of the approaches we mentioned above.
- LabelMe is an open-source online tool that helps users build image databases for computer vision applications and research.
- Sloth is a free tool for labeling image and video files. One of its famous use cases is facial recognition.
- Bella is a tool that is used for text data labeling.
- Tagtog is a startup that provides the same name web tool for automated text categorization.
- Praat is a free software for labeling audio files.