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WorksheetsNatural Language Processing(NLP)
Total questions: 44
Worksheet time: 22mins
What does NLP stand for?
a) Natural Language Programming
b) Natural Linguistic Processing
c) Natural Language Processing
d) Native Language Parser
Which of the following is a major goal of NLP?
a) Compiling programming languages
b) Making computers understand human language
c) Creating hardware interfaces
d) None of the above
Which year did Alan Turing propose the Turing Test?
a) 1943
b) 1950
c) 1960
d) 1975
The first significant NLP system was developed for:
a) Translation
b) Sentiment analysis
c) Voice control
d) Summarization
What was the name of the first machine translation system?
a) ELIZA
b) ALPAC
c) Georgetown Experiment
d) Watson
Which programming language is commonly used for modern NLP applications?
a) Java
b) Python
c) C++
d) COBOL
NLP is a subfield of which broader field?
a) Robotics
b) Artificial Intelligence
c) Networking
d) Databases
Which of the following is not a common NLP task?
a) Parsing
b) Tokenization
c) Object detection
d) Named Entity Recognition
Which NLP task involves identifying people, places, and organizations in text?
a) POS tagging
b) Named Entity Recognition
c) Parsing
d) Tokenization
Which NLP application converts voice to text?
a) Speech synthesis
b) Speech recognition
c) Speech compression
d) Signal filtering
Which is a real-world example of NLP?
a) Face recognition
b) Google Translate
c) AutoCAD
d) ImageNet
Which is used for language generation in NLP?
a) Classification
b) Text summarization
c) POS tagging
d) Parsing
Sentiment analysis is used in which of the following domains?
a) Customer reviews
b) Weather forecasting
c) Network routing
d) DNA sequencing
What is tokenization in NLP?
a) Assigning labels
b) Splitting text into smaller parts
c) Finding nouns
d) Compressing sentences
What is a 'phrase' in NLP?
a) A full paragraph
b) A group of words that act as a unit
c) A single noun
d) A punctuation mark
Lexical ambiguity occurs when:
a) A sentence has multiple meanings
b) A word has multiple meanings
c) Grammar is incorrect
d) There is a spelling mistake
Syntactic ambiguity is caused by:
a) Multiple spellings
b) Lack of data
c) Sentence structure allowing different interpretations
d) Slang words
Which is an example of lexical ambiguity?
a) “I saw the man with a telescope.”
b) “Bank” meaning financial institution or river bank
c) “The old man the boat.”
d) “Flying planes can be dangerous.”
Which is the first step in an NLP pipeline?
a) Stemming
b) Tokenization
c) Parsing
d) Classification
Which is not a step in standard NLP pipelining?
a) Tokenization
b) POS tagging
c) Firewall filtering
d) Named Entity Recognition
What does language modeling try to do?
a) Translate text
b) Predict the next word
c) Remove stopwords
d) Count characters
Which of these is a type of language model?
a) CNN
b) Naive Bayes
c) Bigram
(N-Gram)
d) Ridge regression
What is a unigram model?
a) Considers 1 word at a time
b) Uses 2-word pairs
c) Based on paragraphs
d) Ignores punctuation
Which model considers word order and previous words?
a) Unigram
b) Bigram/Trigram
c) Bag of Words
d) TF-IDF
POS stands for:
a) Probability of Syntax
b) Part-of-Speech
c) Prediction of Structure
d) Parsing of Sentences
What does a POS tagger do?
a) Tags parts of a car
b) Assigns grammatical roles to words
c) Removes stopwords
d) Summarizes text
Which of these is a valid POS tag?
a) NNP
b) TTT
c) SNT
d) VBS
POS tagging is an example of:
a) Classification
b) Clustering
c) Regression
d) Compression
NER stands for:
a) Named External Routing
b) Natural Entity Reduction
c) Named Entity Recognition
d) Name Extension Registry
NER identifies which of the following?
a) Words
b) Sentences
c) People, Places, Organizations
d) Verbs
Which NLP task comes after tokenization and POS tagging in a pipeline?
a) Machine translation
b) Text summarization
c) Named Entity Recognition
d) Spell checking
"Barack Obama" would be labeled in NER as:
a) Date
b) Organization
c) Location
d) Person
HMM stands for:
a) Hidden Markov Model
b) Hybrid Memory Model
c) Hierarchical Model Maker
d) High Memory Map
CRFs are used for:
a) Random predictions
b) Single word classification
c) Sequence labeling
d) Speech synthesis
Which algorithm is used to decode CRFs?
a) Forward algorithm
b) Viterbi algorithm
c) Naive Bayes
d) TF-IDF
Maximum Entropy is also known as:
a) Naive Bayes
b) Logistic Regression
c) Support Vector
d) Decision Tree
MaxEnt models assume:
a) Everything is independent
b) All features are equally important
c) Least biased outcome given known facts
d) Sequence dependence
What do CRFs solve better than HMMs?
a) Memory usage
b) Sequence alignment
c) Label bias problem
d) Feature selection
Which model gives globally best label sequence?
a) MaxEnt
b) HMM
c) CRF
Which would be best for POS tagging with label dependencies?
a) MaxEnt
b) CRFs
c) TF-IDF
What will be the output of the following code?
import spacy nlp = spacy.load("en_core_web_sm") doc = nlp("Apple is looking at buying a startup in the UK") for ent in doc.ents: print(ent.text, ent.label_)
A.
Apple PERSON startup ORG UK LOC
B.
Apple ORG UK GPE
C.
Apple PRODUCT UK COUNTRY
D.
Apple ORG startup GPE UK ORG
What is the main purpose of smoothing in language modeling?
A. To remove stop words
B. To reduce sentence length
C. To handle zero probabilities for unseen events
D. To increase the speed of the algorithm
What does the Good-Turing method adjust?
A. The maximum frequency only
B. The frequency of observed n-grams
C. The probability mass of unobserved events
D. Only bigram counts
What does Kneser-Ney smoothing improve over other techniques?
A. Memory usage
B. Predicting common words
C. Estimating rare or unseen n-grams by considering their diversity of contexts
D. Assigning highest probability to stopwords
