Data Annotation at Netguru
Data Annotation at Netguru
Everything you need to know about data annotation
What is data annotation?
Data annotation is the name given to the process of labeling different types of data, like text, images, and sound.
Data labeling and annotation services are important in the development of AI and machine learning (ML) technologies because they enable ‘supervised learning’.
In supervised learning, data is preprocessed and labeled, which helps the machine to understand and recognize recurring patterns. This is useful for future cases where the algorithm is presented with un-annotated data.
In basic terms, data annotation and labeling helps improve the efficiency and accuracy of machine learning tools. It can be applied to multiple data formats and requires precision and expertise.
What are the types of data annotation?
The most common type of data annotation, text annotation services (or semantic annotation) help AI machine learning languages develop new concepts by using labeled text data as a reference.
Another form of text labeling is entity annotation, the process of labeling unstructured data with useful information that helps the machine learning program make sense of it.
Text annotation can be used to optimize chatbot services, category classification and search engine relevancy, among other things.
Speech recognition tools need annotated audio data to efficiently process sound for applications like virtual assistants or chatbots (think of Siri or automated telephone menus that operate on voice).
Audio annotation can be applied to any sound or speech file metadata. Labels can be added to help define sound types (intonations, phrase types) or be based on author, genre, category etc.
Image annotation services are growing in popularity with the rise of autonomous vehicles and the need for automated content monitoring (e.g. on social media sites).
As with text annotation, useful information is added to the image metadata to train machine learning algorithms to recognize features you want it to process automatically in the future.
Image annotation can be used to help block sensitive content or guide autonomous vehicles/devices in physical spaces.
Video annotation is similar to image annotation, but the process is more complicated because there are so many more images to look at.
Labels you might add to a video could include bounding boxes around a certain part of the video frame or full segmentation, in which each pixel is tracked and labeled with semantic meaning.
What are the advantages of data annotation?
Data annotation in machine learning is becoming more common because it offers benefits of efficiency, accuracy, and output.
With annotated data, AI and machine learning applications can recognize and understand previously obscure data, enabling for continual improvement and more accurate output for the end user.
An example is in search results, where relevant data annotation can enable search engines to produce the desired search for users with only a few characters. Data annotation for eCommerce can also produce more relevant product recommendations.
Better accuracy means better end user experience, which translates to the ability to attract and retain customers. Data annotation software in AI and machine learning helps to build seamless processes in communications, retail, research and manufacturing, to name a few.
This involves real-time issue tracking and feedback, as well as workflow processes like labeling consensus.
Even piece AI and machine learning software requires a human workforce to manage. Human involvement is needed to handle exceptions and quality assurance, so great AI data annotation solutions will also offer workforce management capabilities, such as task assignment and productivity analytics.
This can help in measuring the time your workforce spends completing tasks and levels of accuracy.
Integrated labeling services
Real life data annotation use cases
Data set management
Automated data annotation involves marking and categorizing data using machine learning. This can help improve efficiency of data management and deliver richer data insights to improve overall business models.
Data quality control
Machine learning data annotation ensures the data processed by AI programs is of a high quality.
Machine learning tools can only perform at a high level if the data they use is of premium quality. Data annotation tools help to manage the quality control (QC) and verification process.
With such a broad range of data annotation services and applications, a great data annotation platform should offer integrated labeling services so you can make use of the range of possibilities data annotation offers.
How is data annotation used in machine learning?
Semantic annotation is the process of labeling various concepts within text data, like people, objects, product names & types.
Machine learning tools use semantically annotated metadata to learn how to categorize concepts when new text is fed into the algorithm. As mentioned above, this can help to improve search engine relevance and chatbot features.
Text categorization (sometimes referred to as text classification) assigns categories or tags to text data and organizes it according to content type.
It is a fundamental task to help machine learning models with natural language processing (NLP) and can be used for topic labeling, spam detection or sentiment analysis.
Entity annotation labels unstructured data with machine-readable information. It is used in several machine learning processes. One example is named entity recognition, which classifies named titles in test formats and can cover any predefined classification, such as person, organization or place.
An offshoot of entity annotation is intent extraction, which uses sequential segmentation to help train models to recognize user intent. This enables the optimization of feedback features and chatbots.
For example, it can identify whether a user intends to return a product or unsubscribe from a service, giving you the ability to develop resolution models and respond to negative feedback with more context.
Phrase chunking is the process of tagging parts of speech or text with their relevant grammatical or linguistic meanings. An example is the classification of words or phrases into their language types, like verbs or nouns.
Phrase chunking is useful when you want your machine learning model to extract specific types of information, like locations or a person’s name.
Image & video annotation
Image annotation is the process of labeling or classifying an image using text and annotation tools to show the data features you want your model to recognize, adding metadata to a dataset.
Image annotation is used to recognize objects and boundaries within an image for greater understanding of the image. There are four main types of image annotation: classification (in the output can detect the presence of an object in the image), object detection (in which the output can detect the presence, location and number of objects in the image), semantic segmentation (in which the output can detect the presence and location of an object within certain segments of the image) and instance segmentation (in which the output can detect the presence, location, number, size, and shape of an object within the image).