Natural Language Processing (NLP)

Question 1

What is Natural Language Processing?

(NLP)

Answer 1

It is a field of artificial intelligence focused on enabling computers to understand, interpret, and respond to human language.

Examples of NLP applications include virtual assistants, translation services, and sentiment analysis.

Question 2

True or False:

NLP is concerned with both understanding and generating human language.

Answer 2

True

Natural Language Processing (NLP) involves tasks like interpreting (understanding) and producing (generating) human language.

Question 3

What is tokenization in NLP?

Answer 3

It is the process of breaking down text into smaller units, such as words or sentences, called tokens.

Tokenization is the first step in text processing for many NLP tasks.

Question 4

Fill in the blank:

In tokenization, text is divided into smaller ______.

Answer 4

tokens

Question 5

What are word embeddings?

Answer 5

These are representations of words as vectors in a continuous vector space, capturing semantic meanings and relationships.

Popular word embedding models include Word2Vec and GloVe.

Question 6

True or False:

Word embeddings help capture the context of words in text.

Answer 6

True

Word embeddings represent words as vectors in a way that reflects their meanings and relationships based on context.

Question 7

What is machine translation in NLP?

Answer 7

It is the automatic conversion of text from one language to another using computer algorithms.

Examples include Google Translate and Microsoft Translator.

Question 8

Which NLP task involves summarizing large amounts of text into shorter, coherent summaries?

Answer 8

summarization

Question 9

What does NER stand for in NLP?

Answer 9

Named Entity Recognition

Question 10

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

Answer 10

It aims to identify and categorize key information (entities) in text, such as names, dates, and locations.

Question 11

True or False:

Sentiment analysis is used to determine the emotional tone or attitude expressed in a piece of text.

Answer 11

True

Sentiment analysis classifies text as positive, negative, or neutral to gauge emotional tone or opinion.

Question 12

What is sentiment analysis in NLP?

Answer 12

It is the process of determining whether a piece of text expresses a positive, negative, or neutral sentiment.

Commonly used in social media monitoring and customer feedback analysis.

Question 13

Fill in the blank:

In machine translation, algorithms automatically convert text from one ______ to another.

Answer 13

language

Question 14

Which NLP task would be appropriate for extracting people’s names and locations from a news article?

Answer 14

Named Entity Recognition

(NER)

Question 15

What is a common challenge in machine translation?

Answer 15

Handling idiomatic expressions and nuances of each language effectively.

Idiomatic expressions often do not translate directly across languages, requiring context-aware translation.

Question 16

Explain the difference between tokenization and summarization.

Answer 16

• Tokenization breaks text into smaller parts called tokens.
• Summarization condenses text into a shorter version capturing the main points.

Question 17

Scenario: A company wants to automatically categorize customer feedback into ‘positive’, ‘neutral’, or ‘negative’.

Which NLP technique should they use?

Answer 17

Sentiment analysis

Question 18

True or False:

Word embeddings can help in understanding the relationships between words based on their context in large datasets.

Answer 18

True

By analyzing word co-occurrence in large datasets, embeddings map semantic relationships—placing similar words closer in vector space.