NLP Basics Quiz 2 > Data AI Revolution

NLP Basics Quiz 2

This is the second quiz in the NLP Basics series, featuring new questions about tokenization, sequence-to-sequence tasks, stemming vs. lemmatization, and other core NLP concepts. With 10 multiple-choice questions, some of which have more than one correct answer, this quiz aims to strengthen your foundational understanding of NLP

1 / 10

Which of the following techniques helps improve the performance of language models?

Regularization

Convolution

Lemmatization

Stemming

2 / 10

What is the Bag of Words (BoW) model used for in NLP?

Converting a text into a set of word counts

Translating text between languages

Representing text data in a structured format

Predicting the next word in a sequence

3 / 10

Which of the following are the examples of a sequence-to-sequence NLP task?

Sentiment analysis

Text summarization

Machine translation

Named Entity Recognition (NER)

4 / 10

What is the main difference between stemming and lemmatization?

Stemming converts words to their root form; lemmatization performs sentiment analysis

Stemming is more accurate than lemmatization

Stemming uses a dictionary; lemmatization cuts off prefixes

Stemming removes suffixes; lemmatization converts words to their base form

5 / 10

Which of the following is a challenge in natural language understanding (NLU)?

Parsing structured data

High dimensionality of text data

Ambiguity in human language

Visualizing large datasets

6 / 10

Which of the following are popular pre-trained NLP models?

GPT

BERT

LSTM

ResNet

7 / 10

What is a stop word in NLP?

8 / 10

Which of the following methods can be used for text classification?

Logistic Regression

K-Means

Naive Bayes

Random Forest

9 / 10

What is the primary purpose of tokenization in NLP?

Translating text into another language

Dividing text into words or subwords

Breaking text into sentences

Identifying parts of speech

10 / 10

Which of the following tasks is NOT a common application of NLP?

Image classification

Text summarization

Machine translation

Sentiment analysis

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NLP Basics Quiz 2

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