There are successes in artificial intelligence (AI), but it needs more data to speed up the advancement of many AI-related projects. Likewise, AI should be supplied with intelligent data to train machine learning models at the level developers expect. After all, these learning models are computers, and as most people say, a machine will only be smart if it is supplied with excellent data.
It is challenging to turn raw data into smart data. Fortunately, there is a process to add critical pieces of information to raw data through a learning algorithm—data annotation or, for most people, data labeling.
Definition Of Data Annotation
Data annotation plays a significant role in ensuring that machine learning and AI projects are trained with the correct information. The process gives the initial setup for feeding the machine learning models with the information it needs to understand and differentiate the various inputs to provide accurate outputs.
Need For Human Expertise
The machine learning model needs tons of datasets to continue learning. However, machines need the expertise of humans. The information the machines require comes from humans, who carefully sort through raw data and give them accurate labels (in text form) that the machines can recognize. It is a challenging task that requires time and attention to detail. Machines cannot learn without keeping humans in the loop. You can find teams providing data annotation services or use a data annotation platform, such as those from https://dataloop.ai/solutions/data-annotation/ to help you with your project.
Preparation For Data Annotation
If you have a data annotation project, you should consider several things and provide answers to these questions.
What Is Needed For Data Annotation?
Many types of annotation are available, and the choice depends on what form of data is there. For example, some raw data require video and image annotation.
Others might need content categorization, semantic annotation, or text categorization. If you want to use a data annotation service, determine its importance in achieving the goals of your business. Moreover, identify if one type of data will be enough or you need a combination of several data types.
Will The Annotation Accurately Represent A Specific Domain?
Before the service provider can start the work, you should have a domain vocabulary, data category, and format to determine what you should use. This is called creating an ontology. It is the formal naming and definition of the properties, types, and interrelationships of all the entities that exist for a specific domain. For example, machine learning and artificial intelligence teach the machine to communicate using one language.
How Much Data Do You Need?
Data for machine learning and AI do not have a specific amount, but you should understand that it needs an enormous amount of high-quality data since teaching machines can take months or years to set up. Thus, it is vital to seek help from a domain expert to take care of the annotations and regularly evaluate the accuracy of the annotated data.
Is Your Annotation Accurately Representative Of A Particular Domain?
As part of the process of constructing an ontology, you must first grasp the terminology and structure of the data you wish to employ. In machine learning, ontologies play a crucial role. An ontology is the “formal naming and characterization of the kinds, qualities, and interrelationships of the entities that truly or fundamentally exist for a certain domain of discourse,” according to Wikipedia.
Do You Need Your Annotators To Be Subject Matter Experts?
It is critical to have the correct expert handle annotations depending on the intricacy of the data you are annotating. A large number of firms are using the crowd to annotate simple data, but for more sophisticated data, specialist skills are needed. With complicated legal requirements and agreements from ISDA contracts, for example, legal professionals who can identify and categorize the most relevant material are needed.
A data annotation project requires security protocols because some of the data you will feed the machine model could be sensitive and vital corporate and personal data. Likewise, choose to work with subject matter experts to guarantee accuracy.
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