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Data annotation is unavoidable in any organisation
Businesses with large amounts of unstructured data will find it difficult to simplify their operations or make informed decisions. But well-defined and structured data provides consistent data processing.
Data annotation is critical for Artificial Intelligence (AI) and Machine Learning (ML) implementations.
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Data annotation defined
Data annotation is the process of labelling data available in various formats like text, video or images so that machines can use it. When preparing data for a Machine Learning model, poor quality annotations can result in poor model performance.
With the help of correct tools and approaches, labelled data sets are needed to find repeating patterns in annotated data. When the machine algorithm processes sufficient annotated data, it can detect similar patterns when receiving additional unannotated data.
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Different types of data annotation
- Text annotations. A metadata tag is used to show up properties of a dataset. The data uses tags that emphasise criteria such as keywords, sentences, or different feelings in text, such as "angry", to inform the computer on how to differentiate meaning or emotion behind words.
- Video annotations. Adding metadata to unlabeled video files to develop and train an ML system.
- Image annotations. Classifying photos in a dataset to train an ML model.
- Content annotations. Monitoring and applying a pre-determined set of rules and standards to user-generated contributions to assess if a message is allowed.
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Outsourcing annotation services
Data annotation is a groundbreaking data analysis tool that could help businesses take their operations to new levels.
But annotations are time-consuming and complex and requires a highly skilled workplace that can turn out huge volumes of annotated data. This means that outsourcing to a top data labelling firm can result in considerable advantages in the form of dependable AI engines.
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