NLP MCQ - Morphological Analysis

Morphological Analysis in NLP – HOT MCQs with Instant Answers

Explore how NLP systems analyze the structure of words, identify morphemes, and handle inflectional and derivational forms.

What is morphological analysis?

Morphological analysis is the process of analyzing the internal structure of words to identify their root (stem or lemma) and affixes (prefixes, suffixes, inflections, etc.). It helps NLP systems understand how words are formed, related, and varied grammatically (like tense, number, or case).

Example:
For example, from the word “running”, a morphological analyzer extracts:

• Root: run
• Suffix: -ing (indicating progressive aspect)

1. What is the primary goal of morphological analysis in NLP?

A. To identify sentence boundaries
B. To break words into morphemes and analyze structure
C. To remove stop words
D. To assign part-of-speech tags

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Answer: B. To break words into morphemes and analyze structure

Explanation: Morphological analysis splits words into morphemes (roots, prefixes, suffixes) to reveal structure and grammatical meaning. It supports higher-level NLP tasks like tagging and parsing.

2. Which technique uses vocabulary and grammar to find the base form of a word?

A. Tokenization
B. Stemming
C. Lemmatization
D. Stopword removal

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Answer: C. Lemmatization

Explanation: Lemmatization uses linguistic rules and dictionaries to map words to their correct lemma (“running” → “run”), unlike stemming, which just trims endings (“running” → “runn”).

3. Which component detects morpheme boundaries in a word?

A. POS tagger
B. Morphological parser
C. NER model
D. Syntactic parser

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Answer: B. Morphological parser

Explanation: A morphological parser or segmenter identifies prefixes, roots, and suffixes within words — e.g., “unhappiness” → un + happy + ness.

4. Which type of morphology changes grammatical form without altering word meaning?

A. Inflectional morphology
B. Derivational morphology
C. Compound morphology
D. Agglutinative morphology

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Answer: A. Inflectional morphology

Explanation: Inflectional morphology modifies words to express tense, number, or gender (e.g., “run” → “running,” “cat” → “cats”) without changing their category or meaning.

5. Which morphological process creates new words or changes word class?

A. Inflection
B. Derivation
C. Lemmatization
D. Compounding

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Answer: B. Derivation

Explanation: Derivational morphology forms new words by adding affixes, often changing part of speech — e.g., “teach” → “teacher,” “happy” → “happiness.”

6. A morpheme is:

A. A word’s POS label
B. The smallest meaningful grammatical unit
C. A character-level token
D. A semantic embedding

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Answer: B. The smallest meaningful grammatical unit

Explanation: Morphemes are the smallest meaning-bearing elements in language, such as roots (“book”), prefixes (“un-”), or suffixes (“-ed”).

7. What is the correct sequence in morphological analysis?

A. Parsing → Tokenization → Generation
B. Segmentation → Labeling → Parsing → Generation
C. Lemmatization → POS tagging → Parsing
D. Tokenization → Normalization → Generation

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Answer: B. Segmentation → Labeling → Parsing → Generation

Explanation: Morphological analysis first segments words into morphemes, labels their types, parses their structure, and finally generates valid word forms.

8. Which NLP task directly benefits from morphological analysis?

A. Sentiment analysis
B. POS tagging and machine translation
C. Stopword removal
D. Named Entity Recognition only

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Answer: B. POS tagging and machine translation

Explanation: Morphological knowledge ensures accurate translation of inflected forms and helps disambiguate grammatical roles in POS tagging.

9. Which framework uses Finite-State Transducers (FSTs) to model morphology?

A. Statistical Morphology
B. Finite-State Morphology
C. Neural Morphology
D. Rule-based Lemmatization

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Answer: B. Finite-State Morphology

Explanation: Finite-State Morphology uses FSTs to represent bidirectional mappings between surface and lexical forms, enabling efficient word analysis and generation.

10. The goal of morphological generation is to:

A. Tokenize words
B. Predict sentence structure
C. Generate surface word forms from roots and features
D. Remove affixes

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Answer: C. Generate surface word forms from roots and features

Explanation: Morphological generation converts a lemma and grammatical features (like tense or number) into an inflected surface form — e.g., run + past → “ran.”