DMC Text Mining & Natural Language Processing 1 — Questions and Answers
Question 1: Which NLP technique is used to reduce words to their base or root form by removing suffixes and prefixes?
- Tokenization
- Stemming (Correct answer)
- Vectorization
- Parsing
Correct answer: Stemming
Stemming reduces words to their root form (e.g., 'running' → 'run') by stripping affixes, enabling normalization before text analysis.
Question 2: In text mining, what does TF-IDF stand for?
- Text Frequency–Inverse Document Frequency
- Term Frequency–Inverse Document Frequency (Correct answer)
- Token Frequency–Index Document Factor
- Total Feature–Iterative Data Filter
Correct answer: Term Frequency–Inverse Document Frequency
TF-IDF (Term Frequency–Inverse Document Frequency) weights terms by how often they appear in a document relative to how common they are across the corpus.
Question 3: Which process converts raw text into a list of individual words or tokens?
- Lemmatization
- Stop-word removal
- Tokenization (Correct answer)
- POS tagging
Correct answer: Tokenization
Tokenization splits text into its smallest meaningful units (tokens), which is typically the first step in any text-mining pipeline.
Question 4: What is a 'bag-of-words' model in text mining?
- A model that preserves word order and grammar for analysis
- A representation that counts word occurrences while ignoring order (Correct answer)
- A neural network layer that embeds words into dense vectors
- A clustering method that groups similar documents by topic
Correct answer: A representation that counts word occurrences while ignoring order
The bag-of-words model represents text as an unordered collection of word counts, discarding syntax and position information.
Question 5: Which category of words is typically removed during text preprocessing because they carry little semantic meaning?
- Named entities
- Stop words (Correct answer)
- Bigrams
- Lemmas
Correct answer: Stop words
Stop words (e.g., 'the', 'is', 'and') are filtered out because they appear frequently but contribute minimal discriminative information.
Question 6: Sentiment analysis is best described as which type of text-mining task?
- Information extraction
- Document clustering
- Opinion classification (Correct answer)
- Named entity recognition
Correct answer: Opinion classification
Sentiment analysis classifies text (positive, negative, neutral) based on the author's expressed opinion, making it a classification task.
Question 7: Which dimensionality reduction technique is commonly used in text mining to uncover latent topics in a document collection?
- Principal Component Analysis (PCA)
- Linear Discriminant Analysis (LDA)
- Latent Dirichlet Allocation (LDA) (Correct answer)
- Singular Value Decomposition applied as PCA
Correct answer: Latent Dirichlet Allocation (LDA)
Latent Dirichlet Allocation (LDA) is a generative probabilistic model that discovers hidden topic distributions across a corpus.
DMC Text Mining & Natural Language Processing 1
Which NLP technique is used to reduce words to their base or root form by removing suffixes and prefixes?