Document annotation uses AI technology to find and extract specific details from a document. It revolutionizes text processing by replacing the manual searching process with an easy way to find information, making it readily available and shareable. It involves different types, including:
- Named Entity Annotation – Named entity annotation identifies specific data like names or dates, useful for automatic data extraction in sectors like healthcare.
- Sentiment Annotation – Sentiment annotation categorizes text based on conveyed sentiment or emotion, crucial in legal document processing.
- Semantic Annotation – Semantic document annotation attaches extra metadata about related concepts to a document. It helps in understanding and reusing the content.
Document annotation plays a vital role in AI and machine learning as it does the following:
- Improve their models with high-quality datasets
- Enhances natural language understanding
- Helps you easily extract information
- Facilitates automation in diverse domains to drive innovation
As a fundamental building block in AI and machine learning, document annotation will continue to be crucial as we develop more advanced applications.
Read the full article here: