Nowadays, the volume of available data is ever-growing exponentially, and there is no sign it will slow down anytime soon. Thanks to this incredible growth, businesses now have access to unprecedented volumes of information about customers, products, competitors, suppliers, and partners.
But, missing from all this big data are answers to questions like: What exactly should we do with these signals? How can I act on them? Who else should I share them with? And how accurate are they anyway?
Data annotation tools are essential to answer these questions. Data annotation is the process of attaching labels or other forms of information to data to be effectively used for machine learning.
Data Annotation Tools and Machine Learning
Many different data annotation tools are on the market, but not all are suitable for machine learning tasks. To select the right tool for your needs, you first need to understand what a data annotation tool is and what features to look for.
What are Data Annotation Tools and Machine Learning?
Machine learning is a field of computer science that enables computers to learn from data without being explicitly programmed. To do this, the computer must first be “trained” with a set of data that has been annotated with the appropriate labels.
Data annotation refers to labeling data with the expected category for each instance. Machine learning algorithms can then learn from these annotations and make inferences about new, unknown instances based on their similarity to the annotated examples.
In other words, an annotation algorithm provides a way to label training data that will be used to train a model, which can then apply labels to unseen or test data. Because this is an integral part of machine learning, it is worth testing different tools and finding one that works well for your specific use case.
What’s a Data Annotation Tool?
In computer vision applications, a typical application of a labeling algorithm is identifying objects in images or videos by labeling all objects within an image along with their type.
A data annotation tool will require some GUI to allow users to input instructions or notes about their data.
For example, if an image were submitted with the instructions to identify all humans within it, then a good annotation tool should make marking objects easy by allowing annotations on specific pixels (in case more fine-grained information is needed) and provide tools for drawing around identified objects (e.g., bounding boxes).
In short, a data annotation tool offers a way to insert metadata into your dataset that labels each instance with one or more categories. It provides a GUI for labeling images which can be helpful for humans and includes features to manage data sets, control data quality, and monitor worker productivity.
When choosing a data annotation tool, it is essential to consider the specific needs of your application. The next section will highlight six critical features to look for when evaluating annotation tools.
Six Important Data Annotation Tool Features
When looking for a data annotation tool, there are six essential features to keep in mind: dataset management, annotation methods, data quality control, workforce management, security, and integrated labeling services. We will go over each feature in more detail below.
One of the essential features of an ideal data annotation tool is managing your datasets. Having a good UI for uploading and downloading data, renaming files, viewing file sizes, moving files around within directories, etc., can save you time and improve the productivity of labeling workers by reducing the time they have to spend managing their dataset.
Different annotation tools provide various ways for users to annotate data:
- With pointers or bounding boxes around objects in images.
- Drawing symbols on text files that represent categories of information.
- Clicking checkboxes on web forms corresponding to values of variables within documents (e.g., “this email has 3 attachments”).
- Simply typing notes into a Data Ferret table corresponding to values of variables stored in a database.
Having multiple annotation methods to choose from can help you find one that works well for your specific task and can also be helpful when managing multiple projects which may require different types of annotations.
Data quality control
An easy way to monitor data quality is essential for machine learning applications.
Good annotation tools will provide features to check the input data by automatically checking common problems with labels, using models to compute reliability scores based on the collected metadata, or simply providing users with good feedback about their tasks (e.g., if they click too often in irrelevant areas).
Another useful feature is the ability to use “manual” annotations as ground truth, i.e., examples taken directly from the source data that are used to evaluate the accuracy of an automatic annotation algorithm.
It is often helpful to track data annotation workers’ productivity and work habits. Good annotation tools will provide features for managing worker tasks, viewing worker activity logs, setting up reminders, and sending feedback to workers.
This can help you identify any issues with worker productivity and make sure your data is annotated promptly.
One important consideration when using annotated data is security. You want to ensure that your data is properly protected from unauthorized access and that workers only have access to the specific datasets they are supposed to be working on.
Good annotation tools will provide features for controlling access to data, tracking user activity, and encrypting sensitive data.
Integrated labeling services
Some annotation tools provide integrated labeling services which can help reduce the time and effort needed to get started with annotating your data.
These services can include:
- Pre-built models for common tasks (e.g., sentiment analysis, topic modeling).
- A library of annotations that users can browse and download.
- Worker training and management tools.
- Support for multiple languages.
These data annotation services can save you time and hassle when starting a new annotation project.
When choosing a data annotation tool, it is essential to consider the specific needs of your application. This article has highlighted six critical features to consider when selecting an annotation tool for your particular task.