NER systems are designed to identify and classify named entities in text. Some common types of named entities recognized by NER systems include:

  1. Person Names: NER systems can identify individual names of people, such as "John Smith" or "Anna Lee". These entities may include personal titles like "Dr.", "Mr.", or "Ms.".

  2. Organization Names: NER systems can identify the names of companies, institutions, or organizations, such as "Apple Inc." or "Harvard University".

  3. Location Names: NER systems can identify the names of places, such as cities, countries, or regions, such as "New York", "China", or "Southeast Asia".

  4. Date and Time: NER systems can recognize specific dates, times, or time spans mentioned in text, such as "January 1, 2022" or "10:30 AM".

  5. Monetary Values: NER systems can identify monetary values mentioned, including currency symbols, such as "$10" or "€100".

  6. Percentage and Numeric Values: NER systems can recognize specific numeric values, percentages, or ratios, such as "50%" or "2.5 million".

  7. Product Names: NER systems can identify the names of specific products, such as "iPhone 12" or "Nike Air Jordan".

  8. Event Names: NER systems can recognize the names of specific events, conferences, or competitions, such as "Olympic Games" or "CES 2023".

  9. Miscellaneous: NER systems can also identify other types of named entities, such as government policies, scientific terms, or book titles.

  It's important to note that the specific types of named entities recognized may vary depending on the training data and the specific implementation of the NER system. Overall, NER systems play a crucial role in extracting structured information from unstructured text by identifying and classifying named entities.