Natural language processing basics in Intro to Computing - Cheat Sheet & Quick Revision

Recall & Review

beginner

What is Natural Language Processing (NLP)?

NLP is a field of computer science that helps computers understand, interpret, and generate human language in a way that is meaningful.

beginner

Name two common tasks in NLP.

Two common NLP tasks are: 1) Text classification, which sorts text into categories, and 2) Sentiment analysis, which finds the feeling or opinion in text.

beginner

What is tokenization in NLP?

Tokenization is the process of breaking text into smaller pieces called tokens, like words or sentences, to make it easier for computers to analyze.

intermediate

Why do we use stop words removal in NLP?

Stop words are common words like 'the', 'is', and 'and' that usually don't add much meaning. Removing them helps focus on important words and speeds up processing.

intermediate

Explain the difference between stemming and lemmatization.

Both reduce words to their base form. Stemming cuts words roughly (e.g., 'running' to 'run'), while lemmatization uses vocabulary and grammar to find the correct base word (e.g., 'better' to 'good').

What does tokenization do in NLP?

A Translates text into another language

B Removes all punctuation from text

C Breaks text into smaller parts like words or sentences

D Counts the number of characters in text

Tokenization splits text into tokens such as words or sentences to help computers analyze language.

Which of these is an example of a stop word?

A and

B computer

C learning

D data

Stop words are common words like 'and' that usually don't add much meaning.

What is the main goal of sentiment analysis?

A To translate text into multiple languages

B To remove stop words from text

C To count the number of words in a sentence

D To find the feeling or opinion expressed in text

Sentiment analysis detects emotions or opinions in text, like positive or negative feelings.

Which process uses grammar and vocabulary to find the base form of a word?

A Lemmatization

B Stemming

C Tokenization

D Stop words removal

Lemmatization uses grammar and vocabulary to find the correct base form of a word.

Why do we remove stop words in NLP?

A To make text longer

B To focus on important words and speed up processing

C To translate text

D To add punctuation

Removing stop words helps focus on meaningful words and makes processing faster.

Describe the main steps involved in preparing text data for NLP tasks.

Think about how we break down and clean text so computers understand it better.

Explain how sentiment analysis can be useful in real life.

Consider how businesses or people might want to know what others think or feel.