TEXT AI TECHNOLOGIES > AI FEATURES
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.
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.
In the world of NER APIs, there are many companies offering similar services. However, these services may not all work the same way or be as good as each other. Some might be faster or more accurate, but they might also cost more. It’s a good idea to try out a few different options to see which one works best for you.
By aggregating several providers in one software development platform, Xamun allows you to use different kinds of AI tools for your software.
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