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Named Entity Recognition (NER)

Named Entity Recognition (NER) is a subtask of natural language processing (NLP) that involves identifying and classifying named entities within text into predefined categories such as names of persons, organizations, locations, dates, monetary values, percentages, and more. The goal of NER is to extract structured information from unstructured text by identifying and labeling specific entities mentioned in the text.

Named entities are crucial pieces of information that carry meaning and context in various text documents. NER plays a vital role in a wide range of NLP applications, including information retrieval, question answering, sentiment analysis, text summarization, and knowledge graph construction.

Let's go through an example of Named Entity Recognition (NER) using a sample sentence:

Text: "Barack Obama was born in Honolulu and became the 44th President of the United States."

NER Output:

  • Named Entity: "Barack Obama"

    • Type: Person

  • Named Entity: "Honolulu"

    • Type: Location

  • Named Entity: "44th President of the United States"

    • Type: Title

In this example, the NER system has identified and categorized the named entities in the text. Here's a breakdown of each entity:

  1. "Barack Obama" is recognized as a Person entity. The NER system correctly identifies the name of a person.

  2. "Honolulu" is recognized as a Location entity. The NER system identifies the geographical place mentioned in the text.

  3. "44th President of the United States" is recognized as a Title entity. The NER system identifies a title associated with a person.

NER systems use various linguistic features, context analysis, and trained models to make these identifications. The goal is to extract meaningful information from text by recognizing entities and classifying them into predefined categories like person, location, organization, date, and more.

Keep in mind that NER can sometimes be complex due to linguistic variations, context, and the potential for ambiguous entity mentions. NER models are trained on large annotated datasets to generalize and accurately recognize entities across different texts and domains.

Named Entity Recognition (NER)


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