The machine learning market will grow from $21 billion in 2022 to $209 billion by 2029, with an annual growth rate of 44.1%. Nonetheless, data quality plays a critical role in the performance of machine learning models. With the growing demand for machine learning models, there’s a greater need for quality annotation services.
Data annotation is essentially the process of tagging content such as videos, images, and text to enable machine learning models to classify them. The models can also use the annotated data to generate predictions. When data elements get labeled, ML models can accurately understand whatever they’re going to process. It’s also easier to make decisions and process the available data based on existing knowledge.
When data gets attributed, tagged, and labeled, it’s easier for machine learning models to understand what the data is all about and retain relevant information. Newer information built on existing knowledge can also get processed more quickly, enabling you to make timely decisions. Annotated data is essential because:
- Thanks to their diverse and critical applications, machine learning models always need accurate data.
- Finding high-quality annotation data is a challenge for most machine learning models.
There are various data annotation methods. The annotation type you choose should depend on your data’s form. That said, here are the main types of data annotation.
As the name suggests, this process entails training machines to understand the text. For instance, chatbots may identify user requests from the keywords taught to them and provide solutions. If the annotation text is inaccurate, the machine may provide irrelevant information. Accurate text annotation guarantees a better user experience.
Data points may get assigned to specific sentences and keywords during text annotation. Comprehensive text annotation goes a long way in ensuring effective machine training. Standard methods of text annotation include:
Semantic annotation entails tagging text documents with relevant concepts, making it easier to locate unstructured content. Computers can also interpret and read the relationship between the specific parts of metadata and the resources described by semantic annotation.
Intent annotation entails analyzing the intent behind text or search queries. For instance, the sentence “I’d like to locate my phone” indicates a request. In this case, the intent annotation will analyze the needs behind the text to categorize it as either an approval or a request.
Sentiment annotation tags the emotions in a text so that machines can effortlessly recognize human emotions via words. Typically, machine learning models undergo sentiment annotation training to help them pinpoint the emotion within the text. For instance, when an ML model reads through a product’s customer reviews, it can understand the sentiment and attitude behind the text and label the comments as neutral, negative, or bad.
This data labeling and annotation process entail assigning categories to sentences in a document or even an entire paragraph according to the subject. Afterward, users can easily find whatever information they’re looking for on a website.
It’s critical to distinguish between transcription and annotation. Transcription is the word-for-word conversion of an audio file into text. On the other hand, annotation is the process of labeling and adding metadata to an audio file.
Audio annotation is the process of adding labels to an audio file that describe what is happening in the recording. For example, an annotation might be used to label the sound of a car horn honking, or a person laughing. The process helps machines to understand the context and content of an audio file.
Training AI and ML often consist of labeling images. For instance, ML models often gain a human-like comprehension level with tagged digital images, allowing them to interpret the images they see. In data annotation, objects in all images are often labeled. So, depending on the intended use, the number of labels on each image may increase. The four types of image annotation include:
Here, machines are first trained with annotated images, pinpointing what an image connotes with the pre-determined annotated images.
Object Detection/Recognition is a more granular step in image classification and entails describing an image’s positions and the number of entities. In image classification, labels get assigned to the entire image. With object detection, the objects get labeled separately. An image may be labeled as either night or day. On the other hand, object detection tags the various entities on an image, including trees, tables, etc.
Segmentation is an advanced image annotation process. Segmentation entails dividing an image into multiple segments, called image objects. The division makes it easier to analyze the images more keenly. There are three main types of image segmentation:
- Instance segmentation
Here, each entity on an image can get labeled, allowing you to define critical properties of entities, including position and number.
- Semantic segmentation
This entails labeling similar objects on an image according to properties such as location and size.
- Panoptic Segmentation
When instance and semantic segmentation are combined, you get panoptic segmentation.
Video annotation is the process of labeling various aspects of a video to make it easier for machines to understand. Although similar to image annotation, video annotation is more challenging since the subjects in a video are in motion. This requires analyzing the video frame-by-frame to ensure subjects/objects are appropriately labeled. Tagged object information often includes size, color, etc.
The limitations of computers as visual processors are apparent when you look at how they struggle with context. Computers need to be told what they interpret and get provided with context to make decisions. Annotation makes those connections since it’s the human-led process of labeling content such as video, audio, and images. Machine learning models can recognize the content and make predictions when the content gets labeled.
Data annotation often gets confused with data labeling. The two terms often get used interchangeably to define the process of labeling or tagging data available in different formats. Annotation is essentially the process of labeling data so that machine learning models can effortlessly understand and learn the input data using various algorithms.
On the other hand, data labeling is also referred to as data tagging and entails attaching some meaning to different data sets to train machine learning models. As such, labeling identified single term entities from data sets.
Data annotation machine learning is vital when creating training data for ML models. The annotated data trains ML algorithms to have a human-level perception of the world. It also makes machines smart enough to learn and process new information and have human-like behavior. Annotation enables machines to understand and recognize input data and act accordingly.
Given the incredible rate at which data is created, data annotation is an equally impressive feat. A recent report indicated that by 2025, an average of 463 exabytes of data will be created daily. With data being the lifeblood of the customer experience, the role of annotation data is poised to become even more prominent.
The growth of machine learning and artificial intelligence models have forced businesses across all industries to rethink their operations. Data teams need to use clean and accurate data to train ML models. Machine learning data annotation enhances data accuracy besides delivering better quality training data.
However, data annotation is time-consuming because there’s always a more considerable data set to work with. For this reason, you may want to outsource data annotation services to expert annotators that can consistently increase your output with a high level of quality and accuracy.
It’s important to note that human-annotated data is often of higher quality and more accurate than machine-annotated data.
With a data annotation service like Sigma, you can work with specialists experienced in 2D bounding boxes, landmark and point annotation, semantic segmentation, polygons, and more.
Sigma’s process provides multiple checkpoints to ensure quality and accuracy. We assemble each team specifically for your project.
Sigma’s Service Approach:
- Project Analysis: Our senior team will discuss your project details with you and prepare a proposal that meets your needs. Your project manager will be assigned based on your needs and their skill set and background.
- Guidelines and Requirements: We’ll align on guidelines, review project requirements, and address objectives and deliverables.
- Tools & Procedures Setup: Sigma’s platform and AI-Assisted tools are adapted to your project needs, maximizing throughput and quality. This includes testing and refining the system
- Comprehensive Test: Annotators collect data using the adapted tools and procedures, including QA. Sigma will produce a report outlining results and suggestions.
- Client Feedback: After we share our findings, we work with you to determine the appropriate course of action. Either the guidelines will be updated and tools retested, or we’ll move into the next phase.
- Annotation / Collection at Scale: We train the appropriate annotators based on their experience and move into collection at scale.
- Quality Assessment: We provide data that was prepared using the adapted tools and procedures, including QA. This includes a report on results along with suggestions for improvements to process or code.
Get Better Training Data for Your AI
AI adoption gives your products a competitive edge. Nonetheless, you can only generate value if you use high-quality AI training data. Annotated data is critical for AI and ML projects. The data available in annotated audio, videos, text, or images can help you train the AI and ML algorithms. It’s hard to imagine ML and AI models without sufficient and accurate training data sets. Sigma is the leading data annotation services provider with over 30 years of AI experience. We leverage our extensive expertise to provide automated services to millions of clients globally. Creating human-powered annotated data sets delivers massive value to clients across all industries. Our goal is to help you train your AI and ML models to solve the problem to attain the goals you’ve identified. Contact us today to learn more about our customized solutions