Named Entity Recognition (NER) is a process that involves identifying and classifying named entities within text data, such as names of people, organizations, locations, dates, and more. NER is the ability to extract structured information from unstructured text, enabling AI systems to understand the context and relationships between different entities. NER is a key component in various natural language processing (NLP) applications, as it aids in information extraction, semantic understanding, and knowledge organization.
The NER process typically involves training machine learning models, often using labeled datasets, to recognize patterns and linguistic features associated with different types of entities. These models can then analyze text and determine the boundaries and categories of named entities present. NER’s essence extends to a wide array of applications, from information retrieval and text summarization to chatbots and sentiment analysis. By identifying and categorizing entities, NER enables AI systems to enhance their comprehension of text, offering more accurate and contextually relevant insights.
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