Python Quiz for NLP

Introduction

Delving into the realm of Natural Language Processing (NLP) opens up a fascinating frontier of possibilities. Our Python Quiz for NLP is designed to challenge your knowledge, provide insights, and add a fun twist to your learning journey. Through a series of curated questions, you’ll explore various NLP concepts and Python implementations, growing your comprehension and confidence in handling real-world language processing tasks. Natural Language Processing (NLP) is a field at the intersection of computer science, artificial intelligence, and linguistics. Its goal is to enable computers to understand, interpret, and generate human language in a valuable way.

So, are you ready to put your NLP skills to the test?

Read to prepare: Interview-questions-natural-language-processing-using-python

Python NLP – 02

Level: Advanced

1 / 10

1. Which of these is a supervised learning model commonly used for POS tagging?

a) Hidden Markov Model (HMM)

b) PageRank algorithm

c) K-means clustering

d) Naive Bayes classifier

2 / 10

2. How do you extract all the words from the given text using regular expressions? `re.findall(r”w+”, text)`

a) It tokenizes the sentence.

b) It extracts all the numbers.

c) It extracts all the words.

d) It extracts all the special characters.

3 / 10

3. What does the following code do? `nltk.pos_tag([“I”, “am”, “learning”, “NLP”])`

a) Tokenizes the list of words.

b) Tags each word with its part-of-speech.

c) Translates the sentence into another language.

d) Performs sentiment analysis on the sentence.

4 / 10

4. What is the output of the following code? `nltk.pos_tag([“apple”, “is”, “red”])`

a) A list of tokenized words.

b) A list of tuples with words and their Part-of-Speech tags.

c) A list of lemmatized words.

d) A list of named entities.

5 / 10

5. Which algorithm is commonly used for extractive text summarization?

a) Decision Trees

b) Naive Bayes

c) PageRank

d) K-means

6 / 10

6. How do you generate bigrams for the list of words `[“I”, “love”, “Python”]`?

a) Using `nltk.pos_tag(["I", "love", "Python"])`

b) Using `nltk.word_tokenize(["I", "love", "Python"])`

c) Using `nltk.sent_tokenize(["I", "love", "Python"])`

d) Using `nltk.ngrams(["I", "love", "Python"], 2)`

7 / 10

7. In Python NLP, what does the following code do? `WordNetLemmatizer().lemmatize(“running”, pos=”v”)`

a) It identifies named entities in the string.

b) It tokenizes the string into words.

c) It performs spelling correction.

d) It returns the base form "run" of the word "running".

8 / 10

8. What do you use to get Universal Part-of-Speech tags in NLTK?

a) Using `nltk.word_tokenize()`

b) Setting `lang="en"` in `pos_tag()`

c) Using `nltk.sent_tokenize()`

d) Setting `tagset="universal"` in `pos_tag()`

9 / 10

9. How do you get Part-of-Speech tags for each token in spaCy?

a) Using `token.tag_`

b) Using `token.is_alpha`

c) Using `token.dep_`

d) Using `token.ent_type_`

10 / 10

10. Why is POS tagging often used before lemmatization?

a) To translate the text.

b) To tokenize the text.

c) To perform sentiment analysis.

d) To determine the base form of the word accurately.

Your score is

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