Data Annotation: Types and Use Cases for Machine Learning
It is amazing the number of things machines can be trained to do – from voice recognition to navigation to even playing chess! But for them to achieve these incredible feats, a significant amount of time is put into training them to recognize patterns and relationships between variables. This is the essence of machine learning. Large volumes of data are fed to computers for training, validation, and testing. However, for machine learning to take place, these data sets must be labeled to make the information easier for them to understand; a process known as data annotation.
What is Data Annotation?
Data annotation is the process of making objects of interest understandable to machines through labels, which could take the form of text, audio, or images. It is an essential part of supervised learning in artificial intelligence. For supervised learning, the data must be trained to better the machine’s understanding of the desired results. Take for instance that you want to develop a program to single out dogs in images. You must go through the rigorous process of feeding it with multiple labeled pictures of dogs and “non-dogs” to help the model learn what dogs look like. The program will then be able to compare new images with its existing repository to find out whether an image contains a dog in it.
Though the process is repetitive at the beginning, if enough annotated data is fed to the model, it will be able to process new information without needing new annotated data. For the process to be successful, high quality annotated data is required. This is why most developers choose to use human resources for the annotation process. The process might still be successful if computers are used but when the data is nuanced or sensitive, a human eye is preferred. The higher the quality of annotated data fed to the training model, the higher the quality of the output. It is also important to note that most AI algorithms require regular updates to keep up with changes. Some may be updated as often every day.
Types of Annotation in Machine Learning
1. Semantic annotation
This is the process of attaching additional information to texts. This can be compared to how texts are added to the margins of a book in classical annotation. The machine learning model is then able to pick up on any new information from updated texts. Future data can then be classified, linked, and searched with the annotated data as a template.
How does it work?
First, the text is identified from non-textual sources such as videos or audio. Next, it is analyzed by operations such as part-of-speech tagging, naming entity and sentence splitting in a process known as Natural Language Processing (NLP). The concepts identified from texts are then classified into domain-specific groups like names or numbers to remove any ambiguities. This is the most indispensable part of semantic annotations as it converts the input data into information that can be processed by computers by linking it to an already existing database of knowledge. The extracted data is then linked with the domain knowledge to form new or existing relationships with the knowledge held in the database. Finally, the data is indexed and stored for future reference and use.
2. Image annotation
This aim of image annotation is to make objects recognizable through AI and ML models. It is the process of adding pre-determined labels to images to guide machines in identifying or blocking images. It gives the computer, vision model information to be able to decipher what is shown on the screen. Depending on the functionality of the machine, the number of labels fed to it can vary. Nonetheless, the annotations must be accurate to serve as a reliable basis for learning.
Here are the different types of image annotation:
a. Bounding boxes
This is the most commonly used type of annotation in computer vision. The image is enclosed in a rectangular box, defined by x and y axes. The x and y coordinates that define the image are located on the top right and bottom left of the object. Bounding boxes are versatile and simple and help the computer locate the item of interest without too much effort. They can be used in many scenarios because of their unmatched ability in enhancing the quality of the images.
b. Line annotation
In this method, lines are used to delineate boundaries between objects within the image under analysis. Lines and splines are commonly used where the item is a boundary and is too narrow to be annotated using boxes or other annotation techniques.
c. 3D Cuboids
Cuboids are similar to the bounding boxes but with an additional z-axis. This added dimension increases the detail of the object, to allow the factoring in of parameters such as volume. This type of annotation is used in self-driving cars, to tell the distance between objects.
d. Landmark annotation
This involves the creation of dots around images such as faces. It is used when the object has many different features, but the dots are usually connected to form a sort of outline for accurate detection.
3. Image transcription
This is the process of identifying and digitizing text from images or handwritten work. It can also be referred to as image captioning, which is adding words that describe an image. Image transcription relies heavily on image annotation as the prerequisite step. It is useful in creating computer vision that can be used in the medical and engineering fields. With proper training, machines can be able to identify and caption images with ease using technology such as Optical Character Recognition (OCR).
Use Cases of Data Annotation
When building a big search engine such as Google or Bing, adding websites to the platform can be tedious, since millions of web pages exist. Building such resources requires large pools of data that can be impossible to manage manually. Google uses annotated files to speed up the regular updating of its servers.
Large scale data sets can also be fed to search engines to improve the quality of results. Annotations help to customize the results of a query based on the history of the user, their age, sex, geographical location, etc.
Creation of facial recognition software
Using landmark annotation, machines can be able to recognize and identify specific facial markers. Faces are annotated with dots that detect facial attributes such as the shape of the eyes and nose, face length, etc. These pointers are then stored in the computer database, to be used if the faces ever come into sight again.
The use of this technology has enabled tech companies such as Samsung and Apple to improve the security of their smartphones and computers using face unlock software.
Creation of data for self-driving cars
Although fully autonomous cars are still a futuristic concept, companies like Tesla have made use of data annotation to create semi-autonomous ones. For vehicles to be self-driving, they must be able to identify markers on the road, stay within lane limits, and interact well with other drivers. This can be made possible through image annotation. By making use of computer vision, models can be able to learn and store data for future use. Techniques such as bounding boxes, 3D cuboids and semantic segmentation are used for lane detection, collection, and identification of objects.
Advances in the medical field
New technology in the medical field is largely based on AI. Data annotation is used in pathology and neurology to identify patterns that can be used in making quick and accurate diagnoses. It is also helping doctors pinpoint tiny cancerous cells and tumors that can be difficult to identify visually.
What is the importance of using data annotation in ML?
- Improved end-user experience
When accurately done, data annotation can significantly improve the quality of automated processes and apps, therefore enhancing the overall experience with your products. If your websites make use of chatbots, you can be able to give timely and automatic help to your customers 24/7 without them having to speak to a customer support employee that may be unavailable outside working hours.
In addition, virtual assistants such as Siri and Alexa have greatly improved the utility of smart devices through voice recognition software.
- Improves the accuracy of the output
Human annotated data is usually error-free due to the extensive number of man-hours that are put into the process. Through data annotation, search engines can provide more relevant results based on the users’ preferences. Social media platforms can customize the feeds of their users when annotation is applied to their algorithm.
Data annotation is one of the major drivers of the development of artificial intelligence and machine learning. As technology advances rapidly, almost all sectors will need to make use of annotations to improve on the quality of their systems and to keep up with the trends.
If you’re looking for reliable annotated data for your upcoming AI project, get in touch to see Tarjama’s diverse range of multilingual data annotation services geared for the modern enterprise.