Natural Language Processing Quizzes for Year 13 Computer Science

Question 1

What is the key difference between supervised and unsupervised learning in NLP?

Accepted Answer

Supervised learning requires labeled data, while unsupervised learning does not.

Answer

Unsupervised learning requires labeled data, while supervised learning does not.

Answer

Both require labeled data.

Answer

Neither requires labeled data.

Question 2

What is the distinction between a corpus and a lexicon?

Accepted Answer

A corpus is a collection of real-world texts while a lexicon is a dictionary of words.

Answer

A lexicon is a collection of real-world texts while a corpus is a dictionary of words.

Answer

Both are dictionaries of words.

Answer

Both are collections of real-world texts.

Question 3

What is the primary advantage of machine translation over human translation?

Accepted Answer

Speed

Answer

Accuracy

Answer

Nuance

Question 4

Which of the following is NOT a key aspect of Natural Language Processing (NLP)?

Accepted Answer

Knowledge Representation

Answer

Natural language understanding

Answer

Machine translation

Answer

Text processing

Question 5

Which NLP technique involves identifying and analyzing the grammatical structure of a sentence?

Accepted Answer

Dependency parsing

Answer

Part-of-speech tagging

Answer

Named entity recognition

Answer

Machine translation

Question 6

The primary goal of machine translation is:

Accepted Answer

To translate text from one language to another

Answer

To generate new language content

Answer

To identify the language of a given text

Answer

To classify text into predefined categories

Question 7

Lemmatization in NLP aims to:

Accepted Answer

Reduce words to their base form

Answer

Identify the part of speech of words

Answer

Extract keyphrases from text

Answer

Generate text summaries

Question 8

In NLP, stopwords are:

Accepted Answer

Common words that are ignored during processing

Answer

Keywords that are highly specific to a topic

Answer

Words that indicate the sentiment of a text

Answer

Words that describe the structure of a sentence

Question 9

How has deep learning contributed to the progress of Natural Language Processing (NLP)?

Accepted Answer

Deep learning algorithms have greatly improved the accuracy and efficiency of NLP models.

Answer

Deep learning applications in NLP are mainly restricted to text classification tasks.

Answer

Deep learning models cannot handle large datasets commonly found in NLP.

Answer

Deep learning has had a minimal impact on the development of NLP.

Question 10

What is the primary objective of Named Entity Recognition (NER) in NLP?

Accepted Answer

Identifying and classifying specific entities (e.g., persons, locations) in text

Answer

Summarizing the main points of a text

Answer

Translating text from one language to another

Answer

Classifying text into predefined categories

Question 11

Which algorithm has gained prominence for its effectiveness in machine translation tasks?

Accepted Answer

Transformer

Answer

Naive Bayes

Answer

Support Vector Machines

Answer

Decision Tree

Question 12

Which technique below is often used for tokenization in NLP?

Accepted Answer

Regular expressions

Answer

N-gram analysis

Answer

Part-of-speech tagging

Answer

Stemming

Question 13

What's the primary objective of natural language understanding (NLU)?

Accepted Answer

Uncovering the meaning and intent of a given text

Answer

Automatically generating human-like text

Answer

Translating text between different languages

Answer

Extracting organized data from unstructured text

Question 14

What is the purpose of tokenization in NLP?

Accepted Answer

Splitting text into individual units, e.g., words

Answer

Identifying the part of speech of each word

Answer

Removing stop words from the text

Answer

Stemming words to their root form

Question 15

Which algorithm is commonly used for Named Entity Recognition?

Accepted Answer

Conditional Random Fields

Answer

K-Nearest Neighbours

Answer

Support Vector Machines

Answer

Naive Bayes

Question 16

What is the role of word embeddings in NLP?

Accepted Answer

Representing words as vectors, capturing their semantic similarities

Answer

Identifying the frequency of words in a text

Answer

Extracting keyphrases from a document

Answer

Summarizing long texts

Question 17

Which component is absent from a Natural Language Processing (NLP) system?

Accepted Answer

Image Recognition

Answer

Text Preprocessing

Answer

Natural Language Understanding

Answer

Machine Learning

Question 18

An example of a natural language generation task is:

Accepted Answer

Summarizing a text document

Answer

Translating a text from one language to another

Answer

Classifying a news article into different categories

Answer

Identifying the sentiment of a customer review

Question 19

What is the primary aim of text tokenization in NLP?

Accepted Answer

Breaking down text into smaller units

Answer

Identifying parts of speech

Answer

Extracting specific information

Answer

Representing text as numerical vectors

Question 20

Which technique is commonly used to represent the meaning of words and phrases in NLP?

Accepted Answer

Word embeddings

Answer

Bag-of-words

Answer

Recursive neural networks

Answer

Hidden Markov models

Question 21

What is the purpose of natural language understanding in NLP?

Accepted Answer

Analyzing the structure and meaning of text

Answer

Generating text from given input

Answer

Translating text from one language to another

Answer

Classifying text into different categories

Question 22

What is a significant challenge in NLP when dealing with ambiguous sentences?

Accepted Answer

Determining the intended meaning

Answer

Extracting relevant information

Answer

Representing the sentence as a vector

Answer

Parsing the sentence structure

Question 23

What is the key role of supervised learning in training NLP models?

Accepted Answer

Providing labeled data to guide the model's learning

Answer

Automatically inferring patterns from data

Answer

Generating new data to improve model performance

Answer

Evaluating the accuracy of the trained model

Question 24

Which of the following is NOT a type of neural network commonly used in NLP?

Accepted Answer

Support vector machine

Answer

Convolutional neural network

Answer

Recurrent neural network

Answer

Transformer neural network

Question 25

In machine translation, which technique is commonly used to handle words not found in the training vocabulary, known as out-of-vocabulary words?

Accepted Answer

Substitute them with [UNK]

Accepted Answer

Break them into subwords

Answer

Ignore them

Answer

Replace them with a translation

Question 26

What is a major challenge faced by researchers in the field of Natural Language Processing (NLP)?

Accepted Answer

Bridging the gap between syntactic and semantic understanding

Accepted Answer

Developing models that handle noise and ambiguity effectively

Answer

Creating models that rely on large labeled datasets for training

Answer

Overcoming the computational limitations of current hardware

Question 27

Which of the following is **not** a core subfield of Natural Language Processing (NLP)?

Accepted Answer

Image Processing

Answer

Speech Recognition

Answer

Text Classification

Answer

Machine Translation

Question 28

What is the initial step in NLP where raw text is transformed into a structured digital format for further processing?

Accepted Answer

Text Preprocessing

Answer

Machine Learning

Answer

Feature Extraction

Answer

Tokenization

Question 29

Which subfield of NLP focuses on interpreting and extracting meaning from unstructured text?

Accepted Answer

Natural Language Understanding (NLU)

Answer

Information Retrieval

Answer

Machine Translation

Answer

Natural Language Generation (NLG)

Question 30

What is the primary purpose of using a stemmer in NLP?

Accepted Answer

To reduce words to their base or root form

Answer

To identify named entities within text

Answer

To determine the grammatical part of speech of words

Answer

To translate text between different languages

Question 31

What is the primary goal of feature extraction in NLP?

Accepted Answer

To select and identify the most informative features relevant to a specific task

Answer

To transform the data into a format suitable for machine learning algorithms

Answer

To remove noise and redundancy from the data

Question 32

Which NLP technique is employed to condense and summarize large amounts of text data?

Accepted Answer

Text Summarization

Answer

Speech Recognition

Answer

Machine Translation

Answer

Named Entity Recognition