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

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?

Accepted Answer

Term Frequency–Inverse Document Frequency

Answer

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?

Accepted Answer

Tokenization

Answer

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?

Accepted Answer

A representation that counts word occurrences while ignoring order

Answer

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?

Accepted Answer

Stop words

Answer

Stop words (e.g., 'the', 'is', 'and') are filtered out because they appear frequently but contribute minimal discriminative information.

DMC - Data Mining Certification Practice Test

DMC - Data Mining Certification Practice Test

DMC Text Mining & Natural Language Processing