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 – 01

Level: Advanced

1 / 10

1. In neural networks for NLP, where are word embeddings typically used?

a) In the loss function

b) In the hidden layers

c) In the input layer

d) In the output layer

2 / 10

2. Which function in NLTK is commonly used for named entity recognition?

a) nltk.sent_tokenize()

b) nltk.word_tokenize()

c) nltk.pos_tag()

d) nltk.ne_chunk()

3 / 10

3. What does the “embedding dimension” refer to in word embeddings?

a) The size of the training data

b) The length of the word vector

c) The number of layers in the neural network

d) The number of words in the vocabulary

4 / 10

4. Which of the following is a pre-trained word embedding?

a) Bag-of-Words

b) TF-IDF

c) Naive Bayes

d) GloVe

5 / 10

5. Which Java library is often used for named entity recognition and can be integrated with Python?

a) Jupyter

b) Stanford NER

c) Scikit-learn

d) Beautiful Soup

6 / 10

6. Which Python library provides a straightforward implementation of the Word2Vec model?

a) NLTK

b) spaCy

c) NumPy

d) Gensim

7 / 10

7. How do you compute the similarity between two words using their embeddings?

a) Manhattan distance

b) Euclidean distance

c) Jaccard index

d) Cosine similarity

8 / 10

8. Which spaCy model is generally used for English text entity recognition?

a) de_core_news_sm

b) es_core_news_sm

c) fr_core_news_sm

d) en_core_web_sm

9 / 10

9. What does the following code do? `doc = nlp(“Steve Jobs founded Apple.”); [ent.text for ent in doc.ents]`

a) Tokenizes the text into words.

b) Translates the text into another language.

c) Performs sentiment analysis on the text.

d) Extracts named entities from the text.

10 / 10

10. How do you get the type of named entities using spaCy?

a) Using `doc.tokens`.

b) Using `ent.text` for each `ent` in `doc.ents`.

c) Using `ent.label_` for each `ent` in `doc.ents`.

d) Using `doc.sentiment`.

Your score is

Introduction

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