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AI Technologies > AI Features
Named Entity Recognition
How does Named Entity Recognition work?
Named Entity Recognition (NER) is a technology that identifies and classifies named entities within a text into specific categories. These entities can include names of people, places, organizations, dates, quantities, monetary values, and percentages. By creating these categories, NER helps streamline content search by automatically organizing articles into predefined hierarchies.
Sample Use Cases for Named Entity Recognition
Below are several examples of how NER can be utilized:
Information Retrieval
Search engines can use NER to improve search results by categorizing content based on identified entities, making it easier for users to find relevant information.
Content Categorization
News organizations can implement NER to automatically classify articles by topics or entities, enhancing content organization and discoverability.
Customer Support Automation
Chatbots can leverage NER to identify key entities in customer inquiries, allowing for more accurate and relevant responses.
Market Research Analysis
Businesses can use NER to analyze social media and online reviews, extracting insights related to specific brands, products, or competitors.
Legal Document Review
Law firms can employ NER to identify and categorize entities within legal documents, streamlining the review process and improving efficiency.
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