Which is not one of the seven steps of NLP discussed?

Use ML algorithm to predict outcome

Naive Bayes can be used to classify the language of a text. What does the algorithm rely on to identify language?

Word frequency patterns in each language

Why is Naive Bayes called "naive"

Because it assumes features are independent

Which of the following is a key characteristic of Multinomial Naive Bayes?

It models the frequency of words in text

What does TF-IDF stand for?

Term Frequency-Inverse Document Frequency

What is the main goal of tokenization in NLP?

To break text into smaller components like words or sentences

What is lemmatization in NLP?

Finding the root form of a word

What is the main goal of document classification using Naive Bayes?

To predict the topic or category of a document

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Text Classification & Naive Bayes

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An overview of how naive bayes is applied to text classification and used in machine learning.

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Questions 11

Answers

1
Which is not one of the seven steps of NLP discussed?
Encode Variables
Set random seed
Use ML algorithm to predict outcome
Word Vectorization
2
Naive Bayes can be used to classify the language of a text. What does the algorithm rely on to identify language?
Word frequency patterns in each language
Sentence structure and grammar rules
External language datasets
Text length
3
Which library is commonly used for NLP in Python?
Numpy
NLTK
Matplotlib
TensorFlow
4
Why is Naive Bayes called "naive"
Because it only works on simple problems
Because it uses advanced mathematical models
Because it assumes features are dependent
Because it assumes features are independent
5
Which of the following is a key characteristic of Multinomial Naive Bayes?
It handles continuous data
It works with binary features
It assumes normal distribution of data
It models the frequency of words in text
6
What does TF-IDF stand for?
Token Frequency-Invariant Document Function
Text Format-Indexed Data Function
Term Frequency-Inverse Document Frequency
Term Feature-Indexed Data Frequency
7
What is the main goal of tokenization in NLP?
To remove all punctuation
To break text into smaller components like words or sentences
To make text shorter
To convert text into numerical values
8
What type of data is Bernoulli Naive Bayes best suited for?
Binary Data
Mixed data type
Continuous Data
Random Data
9
Which distribution does Gaussian Naive Bayes assume for features?
Normal
Multinomial
Exponential
Binomial
10
What is lemmatization in NLP?
Finding the root form of a word
Breaking text into sentences
Removing stop words
Counting word frequency
11
What is the main goal of document classification using Naive Bayes?
To reduce the document size
To cluster documents based on content
To predict the topic or category of a document
To extract keywords from documents

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