Recognizing Entities: An Overview of Name Entity Recognition in Natural Language Processing

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Named Entity Recognition (NER) is a subtask of Natural Language Processing (NLP) that involves identifying and classifying named entities in text. Named entities are specific, named objects, locations, organizations, dates, monetary values, percentages, and other types of entities within a text. NER plays a crucial role in various NLP applications, including information retrieval, question answering, and text summarization.

I. Key Concepts in Named Entity Recognition:

1. Types of Named Entities:

• Person: Individual names of people.
• Organization: Names of companies, institutions, etc.
• Location: Geographical entities like cities, countries, etc.
• Date: Temporal expressions including dates and durations.
• Time: Time expressions, including clock time.
• Money: Monetary values and currency names.
• Percentage: Percentage values.
• Miscellaneous: Entities that do not belong to the above categories.

2. NER Approaches:

• Rule-Based Approaches: Use handcrafted rules and…