Natural Language Processing MCQ

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Question 501 : the study of the meaning of words, phrases, and sentences (meaning in a language)

  1. Semantic roles
  2. homonyms
  3. location
  4. semantics
  

Question 502 : What is morphology?

  1. The study of the rules governing the sounds that form words
  2. The study of the rules governing sentence formation
  3. The study of the rules governing word formation
  4. The study of the rules governing the sounds that form sentence
  

Question 503 : How to compute probability of a sentence or sequence of sentence in N-gram model?

  1. P(W) = P(W1,W2, W3,…, Wn)
  2. P(W) = P(Wn+1|Wn-1)
  3. P(W) = P(Wn-1| Wn+1)
  4. P(W) = P(Wn+1 | Wn)
  

Question 504 : In which class, new words are added all the time

  1. Open class
  2. Closed class
  3. Tree bank
  4. WSD
  

Question 505 : If your training loss increases with number of epochs, which of the following could be a possible issue with the learning process?

  1. Regularization is too low and model is overfitting
  2. Regularization is too high and model is underfitting
  3. Step size is too large
  4. Step size is too small
  

Question 506 : The statement “Time passes very quickly” can be represented as

  1. AdvP->(Intens) NP
  2. AdvP->(Intens) Adv
  3. N->Wh-NP VP
  4. S->Wh-NP VP
  

Question 507 : For understanding semantic relation between terms which type of technique used?

  1. Supervised learning
  2. Clustering technique
  3. Ontology based classification
  4. Unsupervised learning
  

Question 508 : Which is not a POS tagging approaches

  1. Rule based POS tagging
  2. Stochastic POS tagging
  3. Transformation based Tagging
  4. Fuzzy logic based Tagging
  

Question 509 : Which two events are used by Hidden Markov model to build probalistic model?

  1. Transitive and Hidden events
  2. Transitive and emisson events
  3. Observed and Hidden events
  4. Emission and Hidden events
  

Question 510 : Parsing determines ___________ (Grammatical Analysis) for a given sentence.

  1. Parse Tree
  2. Parse Graph
  3. Parse Plan
  4. Parse Model
  

Question 511 : For e.g. sentence is "Park the car". POS for words in sentences park: Noun, Verb. the: Determiner. car: Noun. How many hidden state sequences are possible

  1. 4
  2. 8
  3. 7
  4. 2
  

Question 512 : Number,person,gender and case agreements are examples of which types of constraints on reference resolution?

  1. semantic
  2. lexical
  3. discourse
  4. syntactic
  

Question 513 : Which of the following is the example of overstemming?

  1. Univers
  2. Universe
  3. Universal
  4. University
  

Question 514 : Reasoning about time can be facilitated by:

  1. Detection and normalization of temporal expressions.
  2. TimeBank Corpus
  3. Sequence Models
  4. Fixed set of slots
  

Question 515 : To automat HR recruitment process ________type of NLP application will be suitable.

  1. Question Answering System
  2. Machine Transltion
  3. Sentiment Analysis
  4. NER
  

Question 516 : ........... are the lexemes with the same orthographic form but different meaning.

  1. homographs
  2. homophones
  3. synonyms
  4. Hypernyms
  

Question 517 : Which of the following instances the regular expression “\b(one|two|three)\b” can recognize?

  1. “one”
  2. “onetwo”
  3. “TWO”
  4. “THREE”
  

Question 518 : Solve the equation according to the sentence “I am planning to visit New Delhi to attend Analytics Vidhya Delhi Hackathon”. A = (# of words with Noun as the part of speech tag) B = (# of words with Verb as the part of speech tag) C = (# of words with frequency count greater than one) What are the correct values of A, B, and C?

  1. 5, 5, 2002
  2. 5, 5, 2000
  3. 7, 5, 2001
  4. 7, 4, 2002
  

Question 519 : In an HMM, observation likelihoods measure

  1. The likelihood of a POS tag given a word
  2. The likelihood of a POS tag given the preceding tag
  3. The likelihood of a word given a POS tag
  4. The likelihood of a POS tag given two preceding tags
  

Question 520 : Assume a corpus with 350 tokens in it. We have 20 word types in that corpus (V = 20). The frequency (unigram count) of word types “short” and “fork” are 25 and 15 respectively. If we are using the Laplace smoothing, which of the following is PLaplace(“fork”)?

  1. 15/350
  2. 16/370
  3. 30/350
  4. 31/370
  

Question 521 : The phase Syntax Analysis is modeled on the basis of

  1. High level language
  2. Low level language
  3. Context free grammar
  4. Regular grammar
  

Question 522 : PROLOG, LISP, NLP are the language of

  1. Artificial Intelligence
  2. Machine Learning
  3. Internet of Things
  4. Deep Learning
  

Question 523 : Which of the following models can be estimated by maximum likelihood estimator?

  1. Support Vector Machines
  2. Maximum Entropy Model
  3. k Nearest Neighbor
  4. Naive Bayes.
  

Question 524 : Lexical semantics deals with_________

  1. Meaning of word
  2. internal structure of words
  3. relationship between the words
  4. All a,b,c
  

Question 525 : _____________ is the process of understanding if a given text is talking positively or negatively about a given subject (e.g. for brand monitoring purposes).

  1. Syntactical analysis
  2. Hybrid analysis
  3. Sentiment Analysis
  4. Lexical analysis
  

Question 526 : For automated complaint handling ______ type of NLP application can be used.

  1. NER
  2. Machine Transltion
  3. Sentiment Analysis
  4. Text Categorization
  

Question 527 : Meaning Representation Bridges The Gap Between

  1. Linguistic & Commonsense Knowledge
  2. Dictionary & Special Knowledge
  3. Mother Tongue & Commonsense Knowledge
  4. Linguistic & Mother Tongue Knowledge
  

Question 528 : A ___________ is a word that resembles a preposition or an adverb, and that often combines with a verb to form a larger unit called a phrasal verb

  1. Preposition
  2. Determiners
  3. Particle
  4. Adjectives
  

Question 529 : Which of the following is the example of understemming?

  1. Data
  2. Date
  3. Datum
  4. Dat, Datu
  

Question 530 : In Probability Ranking Principal, Ranking documents in order of ____________ probability of relevance is optimal.

  1. Increasing
  2. Decreasing
  3. Anyway
  4. Steady
  

Question 531 : The words ‘there’ and ‘their’ causes which of the following type of ambiguity?

  1. Syntactic
  2. Semantic
  3. Phonological
  4. Pragmatic
  

Question 532 : Correct rule to write noun phrase for the sentence “The boy gave the girl a book”

  1. VP – Verb NP
  2. VP – Verb PP
  3. VP – NP PP
  4. VP – Verb NP NP
  

Question 533 : "Parrot ate the guava as it was ripe" Identify the ambiguity

  1. Noun resolutionx
  2. Adjective resolution
  3. Verb resolution
  4. Pronoun resolution
  

Question 534 : Which Of The Following Is Used To Mapping Sentence Plan Into Sentence Structure?

  1. Text Planning
  2. Sentence Planning
  3. Text Realization
  4. Cosine Similarity
  

Question 535 : “Monkey ate the banana as it was ripe” Identify the dependency checking to resolve the ambiguity of ‘it’

  1. Monkey, banana
  2. Banana, ripe
  3. Monkey, ripe
  4. Ate, banana
  

Question 536 : Uses unidirectional language model for producing word embedding

  1. BERT
  2. GPT
  3. ELMo
  4. Word2Vec
  

Question 537 : Polysemy Is Defined As The Coexistence Of Multiple Meanings For A Word Or Phrase In A Text Object. Which Of The Following Models Is Likely The Best Choice To Correct This Problem?

  1. Random Forest Classifier
  2. Convolutional Neural Networks
  3. Gradient Boosting
  4. Facial Recognition
  

Question 538 : In NLP, computer has to understand natural language in which format

  1. Text and/or speech
  2. Structured format
  3. Unstructured format
  4. XML format
  

Question 539 : _____________ is not a module in question answering system

  1. Question Analysis
  2. Answer Selection
  3. Sentiment Analysis
  4. Information Retrieval
  

Question 540 : Which of the following belongs to the open class group?

  1. Noun
  2. Prepositions
  3. Determiners
  4. Conjunctions
  

Question 541 : Which approach is used for spelling error detection and correction

  1. Script Validation
  2. Tokenization
  3. N-gram
  4. Filteration
  

Question 542 : How given sentence represented using Bigram model? “I want to eat Indian food”

  1. {(I, want), (want, to), (to, eat), (eat, Indian),(Indian, food)}
  2. {(I ), (want, to), (to, eat), (eat, Indian),(Indian, food),(food, I)}
  3. {(I, want, to), (want, to, eat), (to, eat, Indian), (eat, Indian, food)}
  4. {(I), (want), (to), (eat), (Indian), (food)}
  

Question 543 : Assume that there are 10000 documents in a collection. Out of these, 50 documents contain the terms “difficult task”. If “difficult task” appears 3 times in a particular document, what is the TFIDF value of the terms for that document?

  1. 8.11
  2. 15.87
  3. 0
  4. 81.1
  

Question 544 : In NLP, word "natural" indicates

  1. To distinguish human languages from computer languages
  2. It is subfield of AI
  3. It is more close to English language
  4. It is closed to all languages except English language
  

Question 545 : What is the single morpheme of word "Boxes"?

  1. Box
  2. Boxes
  3. Boxses
  4. Boxing
  

Question 546 : Which of the following techniques can be used to compute the distance between two words?

  1. Lemmatization
  2. Part of Speech Tagging
  3. Dekang Lin
  4. N-grams
  

Question 547 : Following property is of - .This POS tagging is based on the probability of tag occurring

  1. Rule based Tagging
  2. Stochastic Tagging
  3. Rule based Tagging and Stochastic Tagging
  4. Neither Rule based Tagging nor Stochastic Tagging
  

Question 548 : Which are the consonants in a given string? “SYZYGEO”

  1. S, Z, G
  2. Y, E
  3. Y, O
  4. S, Y, O, Z, G
  

Question 549 : In the sentence, “They bought a blue house ”, the underlined part is an example of _____.

  1. Noun phrase
  2. Verb phrase
  3. Prepositional phrase
  4. Adverbial phrase
  

Question 550 : For e.g. "Before she purchased it, Mary checked warranty card of the product". In the context of pronoun, this is the example of

  1. Cataphora
  2. Bound
  3. Free
  4. Random