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NLP QUIZ - 3

Total questions: 52

Worksheet time: 26mins

Name
Class
Date
1.

A finite automaton is called “finite” because:

a)

It processes short inputs

b)

It halts early

c)

It has limited memory

d)

It has a finite number of states

2.

In automata theory, the term “automaton” means:

a)

Human-controlled machine

b)

Probabilistic machine

c)

Manual computation

d)

The machine moves automatically

3.

Which 5-tuple formally represents a finite automaton?

a)

(S, T, A, F, R)

b)

(Σ, Q, T, R, P)

c)

(Q, δ, Σ, R, T)

d)

(Q, Σ, δ, q₀, F)

4.

In the 5-tuple, Q represents:

a)

Alphabet

b)

Output symbols

c)

Transitions

d)

Set of states

5.

Σ in an automaton stands for:

a)

State set

b)

Start state

c)

Final state

d)

Alphabet symbols

6.

The transition function in a finite automaton is denoted by:

a)

Q

b)

Σ

c)

F

d)

δ

7.

The initial state is represented by:

a)

F

b)

δ

c)

Σ

d)

q₀

8.

The set of accepting states is written as:

a)

δ

b)

Σ

c)

q₀

d)

F

9.

In DFA diagrams, final states are shown as:

a)

Squares

b)

Filled circles

c)

Triangles

d)

Double circles

10.

DFA transition functions always return:

a)

Two states

b)

No states

c)

Probabilities

d)

Exactly one state

11.

NFA transition functions may return:

a)

Only one state

b)

Zero states only

c)

Final states only

d)

Multiple states

12.

Morphological parsing mainly deals with:

a)

Sentence ordering

b)

Sound patterns

c)

Syntax trees

d)

Breaking words into stems and affixes

13.

The lexical level of a word represents:

a)

Pronunciation

b)

Orthography

c)

Word order

d)

Meaning and structure

14.

The surface level of a word refers to:

a)

Morpheme labels

b)

Lexical tags

c)

Root forms

d)

Written or spoken form

15.

Generation in morphology is the process of:

a)

Tagging POS

b)

Removing affixes

c)

Splitting words

d)

Producing surface words from lexical forms

16.

Which morpheme type is inserted inside a stem?

a)

Prefix

b)

Suffix

c)

Circumfix

d)

Infix

17.

Finite-state transducers are mainly used for:

a)

Parsing syntax trees

b)

Machine translation only

c)

Speech synthesis

d)

Morphological analysis

18.

Minimum edit distance measures:

a)

Grammar similarity

b)

POS accuracy

c)

Semantic closeness

d)

Minimum operations to convert one string into another

19.

POS tagging is the task of:

a)

Segmenting words

b)

Detecting sentiment

c)

Translating languages

d)

Assigning grammatical tags to words

20.

An N-gram is defined as:

a)

A syntax rule

b)

A phonetic unit

c)

A semantic class

d)

A contiguous sequence of N items

21.

A Hidden Markov Model is best described as:

a)

Rule-based grammar

b)

Neural network

c)

Dictionary lookup

d)

Probabilistic sequence model with hidden states

22.

In HMM, the hidden states in NLP usually correspond to:

a)

Letters

b)

Words

c)

Sounds

d)

POS tags

23.

Which application commonly uses finite automata?

a)

Image captioning

b)

Parsing discourse

c)

Topic modeling

d)

Spelling checker

24.

Parsing “books” as book+N+PL is an example of:

a)

Syntax analysis

b)

POS tagging

c)

Segmentation

d)

Morphological parsing

25.

“Internationalization” is derived by adding:

a)

Prefixes only

b)

Circumfixes

c)

Infixes

d)

Suffixes sequentially

26.

Which spelling error inserts an extra character?

a)

Substitution

b)

Omission

c)

Transposition

d)

Insertion

27.

“aer” instead of “are” illustrates:

a)

Omission

b)

Substitution

c)

Insertion

d)

Transposition

28.

POS tagging chooses tags mainly based on:

a)

Word length

b)

Font

c)

Capitalization

d)

Context

29.

The word “book” being noun or verb shows:

a)

Parsing failure

b)

Noise

c)

Overfitting

d)

POS ambiguity

30.

Unigrams correspond to:

a)

Two-word sequences

b)

Three-word sequences

c)

Sentences

d)

Single words

31.

Bigram models predict using:

a)

Three words

b)

Whole paragraph

c)

Letters only

d)

Two-word sequences

32.

In “I like you,” the word with highest probability is chosen based on:

a)

Grammar

b)

Random selection

c)

Alphabetical order

d)

Frequency in corpus

33.

Mobile keyboards rely heavily on:

a)

Syntax rules

b)

Parsing trees

c)

POS tagging only

d)

N-gram prediction

34.

Spell correction using frequent sequences belongs to:

a)

Morphology

b)

Parsing

c)

HMM only

d)

N-gram application

35.

In HMM, observed variables are:

a)

Tags

b)

States

c)

Grammar rules

d)

Words

36.

Transition probability in HMM represents:

a)

Word likelihood

b)

Sentence probability

c)

Corpus size

d)

Probability of moving between states

37.

Emission probability refers to:

a)

State changes

b)

Grammar rules

c)

Transitions

d)

Probability of a word given a state

38.

Dogs/N bark/V comes from combining:

a)

Syntax rules

b)

Dictionary lookup

c)

Random tags

d)

Transition and emission probabilities

39.

NFA is easier to design for recognizing:

a)

Grammar trees

b)

Paragraphs

c)

Corpora

d)

Individual words

40.

Which error replaces a correct letter with a wrong one?

a)

Omission

b)

Transposition

c)

Insertion

d)

Substitution

41.

“Boxes” generated from box+N+PL illustrates:

a)

Parsing

b)

POS tagging

c)

Segmentation

d)

Morphological generation

42.

Circumfixes:

a)

Follow stems

b)

Precede stems

c)

Go inside stems

d)

Surround stems

43.

Which is NOT listed as an N-gram item type?

a)

Characters

b)

Words

c)

Tokens

d)

Images

44.

HMMs are commonly applied in:

a)

Image compression

b)

Web mining

c)

Database indexing

d)

Speech recognition

45.

Which property makes HMM “hidden”?

a)

Words invisible

b)

Corpus unknown

c)

Observations unknown

d)

States are unobserved

46.

The model choosing “you” after “I like” is performing:

a)

Parsing

b)

Tagging

c)

Translation

d)

Prediction

47.

DFA diagrams show transitions using:

a)

Boxes

b)

Tables only

c)

Colors

d)

Labeled arcs

48.

Language identification is an application of:

a)

POS tagging

b)

N-grams only

c)

HMM only

d)

Finite automata

49.

The stem primarily provides:

a)

Tense

b)

Number

c)

Case

d)

Core meaning of the word

50.

Which NLP task is essential for machine translation success?

a)

Image tagging

b)

OCR

c)

Topic modeling

d)

POS tagging

51.

Which option correctly explains the morphological analysis of “cities”?

a)

cities → city + SG

b)

cities → cite + PL

c)

cities → city + V

d)

cities → city + N + PL

52.

Which analysis correctly explains the plural form “geese”?

a)

geese → goose + SG

b)

geese → gee + PL

c)

geese → gees + N

d)

geese → goose + N + PL

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