Recall & Review
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What is Named Entity Recognition (NER)?
NER is a process in machine learning that finds and labels important words or phrases in text, like names of people, places, or dates.
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Name three common types of entities that NER systems identify.
People's names, locations (cities, countries), and organizations (companies, groups) are common entity types in NER.
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Why is NER useful in real life?
NER helps computers understand text better, making tasks like searching, summarizing, or answering questions faster and more accurate.
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What is the output of a Named Entity Recognition model?
The output is the original text with special labels showing which words are entities and what type they are, like <PERSON> or <LOCATION>.
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How does NER handle words that are not entities?
Words that are not entities are usually labeled as O (meaning Outside), showing they don't belong to any special category.
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What does Named Entity Recognition do?
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NER finds and labels important words like names and places in text.
Which of these is NOT a typical entity type in NER?
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Colors are usually not labeled as entities in basic NER tasks.
What label is often used for words that are not entities?
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The label 'O' means the word is Outside any named entity.
Which real-life task can benefit from NER?
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NER helps find names and places quickly in text documents.
What kind of data does NER work on?
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NER analyzes text to find named entities.
Explain what Named Entity Recognition is and why it is useful.
Think about how computers find names or places in sentences.
You got /3 concepts.
Describe the typical output format of a Named Entity Recognition model.
Imagine highlighting words in a sentence with tags.
You got /3 concepts.