Natural Language Processing
Q1. NLP stands for __________
a. New Language Processing
b. Number Language Processing
c. Natural Language Processing
d. Neural Language Processing
Q2. Which of the following is not the domain of Artificial Intelligence?
a. Data Science
b. Computer Vision
c. NLP
d. Data Vision
Q3. Which of the following domain work around numbers and tabular data?
a. Computer Vision
b. Data Science
c. NLP
d. None of the above
Q4. ____ is all about visual data like images and videos.
a. Computer Vision
b. Data Science
c. NLP
d. None of the above
Q5. What is NLP?
a. It works around numbers and tabular data.
b. It is all about visual data like images and videos.
c. It takes in the data of Natural Languages which humans use in their daily lives.
d. None of the above
Q6. Which of the following is not correct about NLP?
a. It is a sub field of AI.
b. It is focused on enabling computers to understand and process human languages.
c. It takes in the data of Natural Languages which humans use in their daily lives.
d. None of the above
Q7. Applications of Natural Language Processing is _________
a. Automatic Summarization
b. Sentiment Summarization
c. Text Summarization
d. All of the above
Q8. Which of the following will help to access a specific, important piece of information from a huge knowledge base.
a. Sentiment Analysis
b. Text classification
c. Virtual Assistants
d. Automatic Summarization
Q9. Automatic summarization is relevant ________________
a. for summarizing the meaning of documents and information.
b. to understand the emotional meanings within the information, such as in collecting data from social media.
c. to provide an overview of a news item or blog post.
d. All of the above
Q10. The goal of _____________ is to identify sentiment among several posts.
a. Sentiment Analysis
b. Automatic Summarization
c. Text classification
d. Virtual Assistants
Natural Language Processing
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Natural Language Processing
Q11. One of the applications of Natural Language Processing is relevant when used to provide an overview of a news item or blog post, while avoiding redundancy from multiple sources and maximizing the diversity of content obtained. Identify the application from the following
a. Sentiment Analysis
b. Virtual Assistants
c. Text classification
d. Automatic Summarization
Q12. Companies use ________ application of NLP , to identify opinions and feelings/emotions online to help them understand what customers think about their products and services.
a. Sentiment Analysis
b. Automatic Summarization
c. Text classification
d. Virtual Assistants
Q13. _____ understands point of view in context to help better understand what’s behind an expressed opinion.
a. Sentiment Analysis
b. Automatic Summarization
c. Text classification
d. Virtual Assistants
Q14. Which of the following makes it possible to assign predefined categories to a document and organize it to help you find the information you need or simplify some activities.
a. Sentiment Analysis
b. Automatic Summarization
c. Text classification
d. Virtual Assistants
Q15. Which of the following is used in spam filtering in E-mail?
a. Sentiment Analysis
b. Automatic Summarization
c. Text classification
d. Virtual Assistants
Q16. Which of the following is not a Virtual Assistant?
a. Alexa
b. Cortana
c. Siri
d. Silvi
Q17. _________________ is a virtual assistant software application developed by Google.
a. Alexa
b. Cortana
c. Google Assistant
d. Siri
Q18. Which of the following is a virtual assistant developed by Microsoft?
a. Siri
b. Cortana
c. Google Assistant
d. Alexa
Q19. What a virtual assistants can do?
a. They can help us in keeping notes of our tasks.
b. They can make calls for us.
c. They can send messages for us.
d. All of the above.
Q20. Which out of the following is the first virtual assistant?
a. Alexa
b. Siri
c. Cortana
d. Google Assistant
Natural Language Processing
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Natural Language Processing
Q21. Which of the following is an example of a voice based virtual assistant?
a. Alexa
b. Siri
c. Cortana
d. All of the above
Q22. _________ is all about how machines try to understand and interpret human language and operate accordingly.
a. Natural Language Processing
b. Data Science
c. Computer Vision
d. None of the above
Q23. Now a days a lot of cases are coming where people are depressed due to reasons like _________
a. Peer Pressure
b. Studies
c. Relationship
d. All of the above
Q24. ________________is considered to be one of the best methods to address stress as it is easy to implement on people and also gives good results.
a. CAD
b. CBT
c. CBD
d. CAM
Q25. CBT stands for ______________
a. Cognitive Behavioural Therapy
b. Common Behavioural Therapy
c. Creative Behavioural Therapy
d. Clear Behavioural Therapy
Q26. Cognitive Behavioural Therapy includes ___________
a. understanding the behaviour of a person in their normal life.
b. understanding the emotions of a person in their normal life.
c. understanding the mindset of a person in their normal life
d. All of the above
Q27. _____ is a technique used by most therapists to cure patients out of stress and depression.
a. CTB
b. CBD
c. CBT
d. BCT
Q28. People who are going through stress will contact _______
a. Psychiatrist
b. Physician
c. Radiologist
d. None of the above
Q29. To understand the sentiments of people, we need to collect their conversational data so the machine can interpret the words that they use and understand their meaning. This step is coming under ______________
a. Problem Scoping
b. Data Acquisition
c. Data Exploration
d. Modelling
Q30. We can collect data by _________________________
a. Surveys
b. Databases available on the internet.
c. Interviews
d. All of the above
Natural Language Processing
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Natural Language Processing
Q31. The most common applications of Natural Language Processing is ______
a. Bat Ball
b. Chat Bot
c. Ro Bot
d. Talk Bot
Q32. Which of the following is a chat bot?
a. Mitsuku Bot
b. CleverBot
c. Jabberwacky
d. All of the above
Q33. Type of chat bot is _______
a. Script bot
b. Smart bot
c. Both of the above
d. None of the above
Q34. ______________ work around a script which is programmed in them.
a. Script bots
b. Smart bots
c. Both of the above
d. None of the above
Q35. _____________ learn with more data.
a. Script bots
b. Smart bots
c. Both of the above
d. None of the above
Q36. Example of smart bot is _____
a. Alexa
b. Siri
c. Cortana
d. All of the above
Q37. Which of the following is not an example of smart bot?
a. Siri
b. Google Assistant
c. Cortana
d. Bots which are deployed in the customer care section and answer the basic queries.
Q38. Our ____ keeps on processing the sounds that it hears around itself and tries to make sense out of them all the time.
a. Eyes
b. Mouth
c. Brain
d. Ear
Q39. Computer understands the language of ______
a. numbers
b. alphabets
c. date
d. None of the above
Q40. The communications made by the machines are very basic and simple. (T/F)
a. True
b. False
Natural Language Processing
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Natural Language Processing
Q41. There are ___________ types of chatbot.
a. 1
b. 2
c. 3
d. 4
Q42. A ____ is a software or computer program that simulates human conversation.
a. Chat bot
b. Robot
c. Talk bot
d. Chatter
Q43. The possible difficulties a machine would face in processing natural language is __________
a. Arrangement of the words and meaning
b. Multiple Meanings of a word
c. Perfect Syntax, no Meaning
d. All of the above
Q44. Syntax refers to the ___ of a sentence
a. Grammatical structure
b. Hindi meaning
c. Pronunciation
d. None of the above
Q45. _______ allows the computer to identify the different parts of a speech.
a. part-of tagging.
b. part-of-sound tagging.
c. part-of-speech tagging.
d. part-of-speak tagging.
Q46. The following line refers to ____________
5 + 6 = 6 + 5
a. Different syntax, same semantics
b. Same syntax, Different semantics
c. Different syntax, Different semantics
d. None of the above
Q47. The following line refers to ____________
2/3 (Python 2.7) ≠2/3 (Python 3)
a. Different syntax, same semantics
b. Same syntax, Different semantics
c. Different syntax, Different semantics
d. None of the above
Q48. Semantics refers to _________
a. grammar of the statement
b. meaning of the statement
c. Both of the above
d. None of the above
Q49. In __________ it is important to understand that a word can have multiple meanings and the meanings fit into the statement according to the context of it.
a. Natural Language
b. Computer language
c. Machine Language
d. None of the above
Q50. In Human language, a perfect balance of ______ is important for better understanding.
a. Syntax
b. Semantics
c. Both of the above
d. None of the above
Natural Language Processing
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Natural Language Processing
Q51. _________________ helps in cleaning up the textual data in such a way that it comes down to a level where its complexity is lower than the actual data.
a. Data Normalisation
b. Text Normalisation
c. Number Normalisation
d. Table Normalisation
Q52. The term used for the whole textual data from all the documents altogether is known as _____
a. Complete Data
b. Slab
c. Corpus
d. Cropus
Q53. Which of the following is the first step for Text Normalisation?
a. Tokenisation
b. Sentence Segmentation.
c. Removing Stopwords, Special Characters and Numbers.
d. Converting text to a common case.
Q54. In ___________ the whole corpus is divided into sentences.
a. Tokenisation
b. Sentence Segmentation
c. Removing Stopwords, Special Characters and Numbers
d. Converting text to a common case
Q55. In Tokenisation each sentence is then further divided into __________
a. Token
b. Character
c. Word
d. Numbers
Q56. Under ______, every word, number and special character is considered separately and each of them is now a separate token.
a. Sentence Segmentation
b. Removing Stopwords, Special Characters and Numbers
c. Converting text to a common case
d. Tokenisation
Q57. __________ are the words which occur very frequently in the corpus but do not add any value to it.
a. Special Characters
b. Stopwords
c. Roman Numbers
d. Useless Words
Q58. Which of the following is an example of stopword?
a. a
b. an
c. and
d. All of the above
Q59. During Text Normalisation, which step will come after removing Stopwords, Special Characters and Numbers.
a. Converting text to a common case.
b. Stemming
c. Lemmatization
d. Tokenisation
Q60. During Text Normalisation, when we convert the whole text into a similar case, we prefer ____________
a. Upper Case
b. Lower Case
c. Title Case
d. Mixed Case
Natural Language Processing
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Natural Language Processing
Q61. _____________ is the process in which the affixes of words are removed and the words are converted to their base form.
a. Lemmatization
b. Stemming
c. Both of the above
d. None of the above
Q62. After stemming, the words which we get after removing the affixes is called _________
a. Stemmed Words
b. Stemma
c. Fruit Word
d. Shoot Word
Q63. While stemming healed, healing and healer all were reduced to _______________
a. heal
b. healed
c. heale
d. hea
Q64. While stemming studies was reduced to ___________________ after the affix removal.
a. studi
b. study
c. stud
d. studys
Q65. After Lemmatization, the words which we are get after removing the affixes is called _________
a. Lemmat
b. Lemma
c. Lemmatiz
d. Lemmatiza
Q66. Which of the following statement is not correct?
a. Lemmatization makes sure that lemma is a word with meaning.
b. Lemmatization takes a longer time to execute than stemming.
c. Stemmed word is always meaningful.
d. Both Stemming and lemmatization process remove the affixes.
Q67. ___________ is a Natural Language Processing model. In this we get the occurrences of each word and construct the vocabulary for the corpus.
a. Bag of Words
b. Bag of Alphabets
c. Bag of Characters
d. Bag of Numbers
Q68. Which of the following things we are getting after ‘Bag of Words’ algorithm?
a. A vocabulary of words for the corpus.
b. The frequency of these words.
c. Both of the above.
d. None of the above
Q69. Expand TFIDF
a. Term Format & Inverse Document Frequency
b. Term Frequency & Inverse Document Frequency
c. Term Frequency & Inverse Data Frequency
d. Term Frequency & Inner Document Frequency
Q70. Bag of words algorithm gives us the frequency of words in each document. It gives us an idea that if the word is occurring more in a document, __________
a. its value is more for that document
b. its value is less for that document
c. its value is not more not less for that document
d. its has no value for that document.
Natural Language Processing
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Natural Language Processing
Q71. Steps to implement bag of words algorithm is given below. Choose the correct sequence.
1. Text Normalisation
2. Create document vectors
3. Create document vectors for all the documents
4. Create Dictionary
a. 1, 2, 3, 4
b. 2, 3, 1, 4
c. 1, 4, 2, 3
d. 1, 4, 3, 2
Q72. _______ are the words which occur the most in almost all the documents.
a. And
b. The
c. This
d. All of the above
Q73. Those words which are a complete waste for machine as they do not provide any information regarding the corpus are termed as _________________
a. Start words
b. End words
c. Stop words
d. Close words
Q74. Which of the following type of words have more value in the document of the corpus?
a. Stop words
b. Frequent words
c. Rare words
d. All of the above
Q75. Which of the following type of words have more frequency in the document of the corpus?
a. Stop words
b. Frequent words
c. Rare words
d. All of the above
Q76. ________ is the frequency of a word in one document.
a. Term frequency
b. Inverse Document Frequency
c. Document Frequency
d. Inverse Frequency
Q77. __________ is the number of documents in which the word occurs irrespective of how many times it has occurred in those documents.
a. Term frequency
b. Inverse Document Frequency
c. Document Frequency
d. Inverse Frequency
Q78. In _________, we put the document frequency in the denominator while the total number of documents in the numerator.
a. Inverse Frequency
b. Inverse Document
c. Inverse Document Frequency
d. Term Frequency
Q79. Which of the following is an application of TFIDF?
a. Document Classification
b. Topic Modelling
c. Stop word filtering
d. All of the above
Q80. ________ helps in removing the unnecessary words out of a text body.
a. Document Classification
b. Topic Modelling
c. Stop word filtering
d. Information Retrieval System
Natural Language Processing
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Natural Language Processing
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