Practice Named Entity Recognition (ner) (27.3.3) - Concepts of Natural Language Processing (NLP)

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9 questions

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Exercise
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Quiz
3 questions · about 2 min · pass mark 70%. Hints available.
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6 sample questions

Question 1.
What does NER stand for?
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Think about the meaning of each word in the acronym.
Question 2.
Give an example of an entity that NER might identify.
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Consider common nouns you encounter.
3Question 3.
What is the primary goal of Named Entity Recognition?
- To translate languages
- To classify names in text
- To analyze sentiment
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Think about what NER stands for.
4Question 4.
True or False: NER can misclassify terms based on context.
- True
- False
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Reflect on examples like 'bat.'
Question 5.
Design a scenario where NER could fail and explain how this could impact data analysis.
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Consider the language diversity in informal communication.
Question 6.
Create a mini-project that implements NER on a specific dataset, ensuring to handle ambiguous terms. Describe your approach.
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Think of model enhancements or extra training data.

5 more questions in this section. Enroll free to practise them all.