Q.1
Morphological Analysis
  • Does discourse analysis
  • Separate words into individual morphemes and identify the class of the morphemes
  • Is an extension of propositional logic
  • None of the above
Q.2
What does NLP stand for? Write what the acronym means.
  • Natural Language Processing
  • Nationwide Loan Processing
  • Netware Lite Protocol
  • None of these
Q.3
Consider the following piece of codefromnltk.corpus import wordnet as wn from nltk.corpus import wordnet_ic brown_ic = wordnet_ic.ic('ic-brown.dat') tree = wn.synsets("tree") plant = wn.synsets("plant")print(tree[0].lin_similarity(plant[0], brown_ic))Tick the statements which are true in this context.
  • Calculates the similarity between the words ‘tree’ and ‘plant’.
  • You cannot find the similarity between two words without using WordNet.
  • A corpus is a large and structured set of machine-readable texts that have been produced in a natural communicative setting.
  • Word similarity is dependent on the context in which it is used
Q.4
In a word cloud, what does the size correspond to?
  • Length
  • Frequency
  • Importance
  • Relation
Q.5
N-grams are defined as the combination of N keywords together. How many bi-grams can be generated from given sentence:“NPTEL videos are a great source to learn engineering courses”
  • 7
  • 8
  • 9
  • 10
  • 11
Q.6
Which of the following sense for the word “language” is not available in wordnet? i. a systematic means of communicating by the use of sounds or conventional symbol ii. communication by word of mouth iii. the cognitive processes involved in producing and understanding linguistic communication iv. the style of a piece of writing or speech v. the mental faculty or power of vocal communication
  • 3
  • 5
  • 4
  • None of these
Q.7
NLP is concerned with the interactions between computers and human (natural) languages.
  • True
  • False
Q.8
What is the field of Natural Language Processing (NLP)?
  • Computer Science
  • Artificial Intelligence
  • Linguistics
  • All of the mentioned
Q.9
____________ is a Python library to make programs that work with natural language.
  • Keras
  • NLTK
  • Pandas
  • Seaborn
  • BeautifulSoup
Q.10
Tick whichever is an application Named Entity Recognition (NER)
  • Classifying content for NEWS providers
  • Efficient Search Algorithms
  • Analysis the rude behavior from customer feedback
  • All of above
Q.11
Given a stream of text, Named Entity Recognition determines which pronoun maps to which noun.
  • False
  • True
Q.12
Consider the sentence: “The touch screen was cool; however, the voice quality and battery were very poor”. Which of the following are true?
  • Aspect: “touch screen”, Sentiment: Positive, Opinion Phrase: “cool”.
  • Aspect: “voice quality”, Sentiment: Negative, Opinion Phrase: “very poor”.
  • Aspect: “battery”, Sentiment: Negative, Opinion Phrase: “very poor”.
  • All of above
Q.13
Which of the following Affective States does Sentiment Analysis mostly focus on?
  • Mood
  • Personality Traits
  • Attitudes
  • Emotion
Q.14
While working with context extraction from a text data, you encountered two different sentences: The tank is full of soldiers. The tank is full of nitrogen. Which of the following measures can be used to remove the problem of word sense disambiguation in the sentences?
  • Compare the dictionary definition of an ambiguous word with the terms contained in its neighborhood
  • Co-reference resolution in which one resolute the meaning of ambiguous word with the proper noun present in the previous sentence
  • Use dependency parsing of sentence to understand the meanings
  • None of these
Q.15
What are the possible features of a text corpusCount of word in a document Boolean feature – presence of word in a document.Vector notation of word Part of Speech Tag Basic Dependency Grammar Entire document as a feature
  • 1
  • 12
  • 123
  • 1234
  • 12345
Q.16
Difficulties/Challenges in Word Sense Disambiguation (WSD) .Tick which is (FALSE) from the statements given below
  • to decide the sense of the word because different senses can be very closely related.
  • Completely different algorithm might be needed for different applications.
  • The problem of Inter-judge variance as the WSD systems are generally tested by having their results on a task compared against the task of human beings
  • Words can be easily divided into discrete sub-meanings.
Q.17
Which of the following regular expression can be used to identify date(s) present in the text object?“The next meetup on data science willbe held on 2017-09-previously it happened on 31/2016”
  • \d{4}-\d{2}-\d{2}
  • (19|20)\d{2}-(0[1-9]|1[0-2])-[0-2][1-9]
  • (19|20)\d{2}-(0[1-9]|1[0-2])-([0-2][1-9]|3[0-1])
  • None of the above
Q.18
Tick what is true about WordNet from the following sentences
  • A hierarchically organized lexical database
  • A machine-readable thesaurus, and aspects of a dictionary
  • Is a lexical database of semantic relations between words
  • All of above
Q.19
Consider the following given sentences. Match the lexical relations between the first word (w​1​) to the second word (w​2​) i.e. w​is a <lexical relation> of w​· Invention of the wheel​ is one of the landmarks in the history of mankind.· Companies are trying to make driverless car.· Golden daffodils​ are fluttering and dancing in the breeze.· Mumbai has unique flower ​park.Holonym i.wheel-carHyponym ii.car-wheelMeryonym iii.daffodils-flowerHypernym iv. flower- daffodils
  • 1-iii, 2-ii, 3-iv, 4-i
  • 1-ii, 2-iii, 3-i, 4-iv
  • 1-ii, 2-iii, 3-iv, 4-i
  • 1-i, 2-ii, 3-iii, 4-iv
Q.20
Morphotacticsis a model of
  • Spelling modifications that may occur during affixation
  • How and which morphemes can be affixed to a stem
  • All affixes in the English language
  • N-grams of affixes and stems
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