DMC Text Mining & Natural Language Processing

Question 1

Which NLP technique is used to reduce words to their base or root form by removing suffixes and prefixes?

Accepted Answer

Stemming

Answer

Tokenization

Answer

Vectorization

Answer

Parsing

Question 2

In text mining, what does TF-IDF stand for?

Accepted Answer

Term Frequency–Inverse Document Frequency

Answer

Text Frequency–Inverse Document Frequency

Answer

Token Frequency–Index Document Factor

Answer

Total Feature–Iterative Data Filter

Question 3

Which process converts raw text into a list of individual words or tokens?

Accepted Answer

Tokenization

Answer

Lemmatization

Answer

Stop-word removal

Answer

POS tagging

Question 4

What is a 'bag-of-words' model in text mining?

Accepted Answer

A representation that counts word occurrences while ignoring order

Answer

A model that preserves word order and grammar for analysis

Answer

A neural network layer that embeds words into dense vectors

Answer

A clustering method that groups similar documents by topic

Question 5

Which category of words is typically removed during text preprocessing because they carry little semantic meaning?

Accepted Answer

Stop words

Answer

Named entities

Answer

Bigrams

Answer

Lemmas

Question 6

Sentiment analysis is best described as which type of text-mining task?

Accepted Answer

Opinion classification

Answer

Information extraction

Answer

Document clustering

Answer

Named entity recognition

Question 7

Which dimensionality reduction technique is commonly used in text mining to uncover latent topics in a document collection?

Accepted Answer

Latent Dirichlet Allocation (LDA)

Answer

Principal Component Analysis (PCA)

Answer

Linear Discriminant Analysis (LDA)

Answer

Singular Value Decomposition applied as PCA