Natural Language Processing Class 10 MCQ – The CBSE has changed the syllabus of Std. X. The MCQs are made based on the new syllabus and based on the New CBSE textbook, Sample paper and Board Paper. All the important Information are taken from the Artificial Intelligence Class X Textbook Based on CBSE Board Pattern.
Natural Language Processing Class 10 MCQ
1. Natural Language Processing majorly deals with__________ processing.
a. Numeric data
c. Image data
b. Textual data
d. Visual data
2. __________is an NLP tool to express an opinion, whether the underlying sentiment is positive, negative, or neutral.
a. Text Classification
b. Machine Translation
c. Sentiment Analysis
d. Automatic Text Summarization
3. What is the first stage of Natural Language Processing (NLP)?
a. Semantic Analysis
c. Lexical Analysis
b. Pragmatic Analysis
d. Syntactic Analysis
4. Words that we want to filter out before doing any analysis of the text are called__________.
a. Rare words
c. Frequent words
b. Stop words
d. Filter words
5. What does discourse integration involve in the context of sentence formation?
a. Identifying individual words in a sentence
b. Forming a coherent story within a sentence
c. Establishing relationships between preceding and succeeding sentences
d. Applying punctuation and grammar rules to a sentence
6. What is the primary challenge faced by computers in understanding human languages?
a. Complexity of human languages
b. Lack of computational power
c. Incompatibility with numerical data
d. Limited vocabulary
7. How do voice assistants utilize NLP?
a. To analyze visual data
b. To process numerical data
c. To understand natural language
d. To execute tasks based on computer code
8. Which of the following is NOT a step in Text Normalisation?
a. Tokenization
b. Lemmatization
c. Punctuation removal
d. Document summarization
9. In the context of text processing, what is the purpose of tokenisation?
a. To convert text into numerical data
b. To segment sentences into smaller units
c. To translate text into multiple languages
d. To summarize documents for analysis
10. What distinguishes lemmatization from stemming?
a. Lemmatization produces meaningful words after affix removal, while stemming does not.
b. Lemmatization is faster than stemming.
c. Stemming ensures the accuracy of the final word.
d. Stemming generates shorter words compared to lemmatization.
11. What is the primary purpose of the Bag of Words model in Natural Language Processing?
a. To translate text into multiple languages
b. To extract features from text for machine learning algorithms
c. To summarize documents for analysis
d. To remove punctuation marks from text
12. In the context of text processing, what are stop words?
a. Words with the frequent occurrence in the corpus
b. Words with negligible value that are often removed during preprocessing
c. Words with the lowest occurrence in the corpus
d. Words with the most value added to the corpus
13. What is the characteristic of rare or valuable words in the described plot?
a. They have the highest occurrence in the corpus
b. They are often considered stop words
c. They occur the least but add the most value to the corpus
d. They are typically removed during preprocessing
14. What information does the document vector table provide?
a. The frequency of each word across all documents
b. The frequency of each word in a single document
c. The total number of words in the entire corpus
d. The average word length in the entire corpus
15. What is the primary purpose of TFIDF in text processing?
a. To identify the presence of stop words in documents
b. To remove punctuation marks from text
c. To identify the value of each word in a document
d. To translate text into multiple languages
16. Assertion: Pragmatic analysis in natural language processing (NLP) involves assessing sentences for their practical applicability in real-world scenarios.
Reasoning: Pragmatic analysis requires understanding the intended meaning behind sentences and considering their practical or logical implications, rather than solely relying on literal word meanings obtained from semantic analysis.
a. Both Assertion and Reasoning are true, and Reasoning is the correct explanation of the Assertion.
b. Assertion is true, but Reasoning is false.
c. Both Assertion and Reasoning are true, but Reasoning is not the correct explanation of the Assertion.
d. Assertion is false, but Reasoning is true.
17. Assertion: Converting the entire text into lowercase following stop word removal is a crucial preprocessing step in natural language processing.
Reasoning: This process ensures uniformity in word representation, preventing the machine from treating words with different cases as distinct entities, thereby enhancing the accuracy of subsequent text analysis.
a. Both Assertion and Reasoning are true, and Reasoning is the correct explanation of the Assertion.
b. Assertion is true, but Reasoning is false.
c. Both Assertion and Reasoning are true, but Reasoning is not the correct explanation of the Assertion.
d. Assertion is false, but Reasoning is true.
13. _ refer to the type of features that we want to collect.
Show Answer ⟶14. Identify the given Chat bot type: It learns from its environment and experience. It also builds on its capabilities based on the knowledge. These can collaborate with humans, working along-side them and learning from their behavior.
Show Answer ⟶15. Observe the given graph and fill in the blank:
_ the neural network, better is the performance.
16. This real life application of NLP is used to provide an overview of a news item or blog post, which avoiding redundancy from multiple sources and maximising the diversity of content obtained. Which is this application? (CBSE 2023 – 2024)
a. Chatbot
b. Virtual Assistant
c. Sentiment Analysis
d. Automatic Summarisation
17. Rajat has made a model which predicts the performance of Indian Cricket players in upcoming matches. He collected the data of players’ performance with respect to stadium, bowlers, opponent team and health. His model works with good accuracy and precision value. Which of the statement given below is incorrect?
a. Data gathered with respect to stadium, bowlers, opponent team and health is known as Testing Data.
b. Data given to an AI model to check accuracy and precision is Testing Data.
c. Training data and testing data are acquired in the Data Acquisition stage.
d. Training data is always larger as compared to testing data.
18. __ means a picture element which is the smallest unit of information that makes up a picture.
a. Vision
b. Pics
c. Pixel
d. Piskel
19. Which of the following words represent an example of a lemma resulting from lemmatisation for “caring” in context to Natural Language Processing (NLP)? (CBSE 2023 – 2024)
a. Care
b. Cared
c. Cares
d. Car
20. Which algorithms result in two things, a vocabulary of words and frequency of the words in the corpus?
a. Sentence segmentation
b. Tokenisation
c. Bag of words
d. Text normalisation
21. What do you mean by syntax of a language?
a. Meaning of a sentence
b. Grammatical structure of a sentence
c. Semantics of a sentence
d. Synonym of a sentence
22. Which one of the following scenario result in a high false positive cost?
a. viral outbreak
b. forest fire
c. flood
d. spam filter
23. Which of the following is correct about the rule based approach?
a. We cannot provide enough rules to the machine.
b. A drawback/feature for this approach is that the learning is static.
c. Once the rules are fed into the system, it takes into consideration any changes made in the original training dataset.
d. It can improve itself based on the feedbacks.
24. A business problem wherein we categorize whether an observation is “Safe,” “At Risk,” or “Unsafe” is an example of _____
a. Classification
b. Clustering
c. Regression
d. Dimensionality Reduction
25. The basis of decision making depends upon
i) availability of information
ii) past experience
iii) positive attitude
iv) self-awareness
a. i) and ii)
b. ii) and iv)
c. i), ii) and iv)
d. i), ii) and iii)
26. Assertion (A): Anyone can kick an artificially intelligent machine
Reason (R): They have no pain receptors
a. Both A and R are correct and R is the correct explanation of A
b. Both A and R are correct but R is NOT the correct explanation of A
c. A is correct but R is not correct
d. A is not correct but R is correct.
27. _________is a term used for any word or number or special character occurring in a sentence. (Token / Punctuator) (CBSE 2022 – 2023)
Show Answer ⟶28. When the prediction matches the reality, the condition is termed as __. (CBSE 2022 – 2023)
Show Answer ⟶29. Which of the following is true about neural networks?
a. Neural Networks tend to perform better with larger amounts of data.
b. Neural Networks tend to perform poorer with larger amounts of data.
c. Neural Networks tend to perform better with smaller amounts of data.
d. Neural Networks need no data
30. Choose the correct option
a. Unsupervised learning ->labelled dataset, Regression
b. Supervised learning -> labelled data set, Regression
c. Unsupervised learning ->unlabelled dataset, Classification
d. Supervised learning -> unlabelled data set, Regression
31. Which of the following is the correct feature of Neural network? (CBSE 2022 – 2023)
a. It can improve the efficiency of two models.
b. Itis useful with small dataset.
c. They are modelled on human brains and nervous system.
d. They need human intervention.
32. A scenario is given to you below. Read it and answer the questions that follow:
Late one night, a car ran over a pedestrian in a narrow by street and drove away without stopping. A policeman who saw the vehicle leave the scene of the accident reported it moving at very high speed. The accident itself was witnessed by six bystanders. They provided the following conflicting accounts of what had happened: – It was a Toyota and its headlights were turned off; – It was a grey Audi. – It was a red car driven by a woman; – The car was moving at high speed and its headlights were turned off; – The car did have license plates; it wasn’t going very fast; – The car didn’t have license plates; the driver was a man; When the car and its driver were finally apprehended, it turned out that only one of the six eyewitnesses gave a fully correct description. Each of the other five provided one true and one false piece of information. Keeping that in mind, can you determine the following:
i) What was the car’s brand?
ii) What was the colour of the car?
iii) Was the car going fast or slow?
iv) Did it have license plates?
v) Were its headlights turned on?
vi) Was the driver a man or a woman?
a. i) -> TOYOTA ; ii) -> GREY ; iii) -> FAST ; iv) -> NO ; v) -> NO ; vi) -> WOMAN
b. i) -> AUDI ; ii) -> RED ; iii) -> SLOW ; iv) -> NO ; v) -> YES ; vi) -> WOMAN
c. i) -> AUDI ; ii) -> RED ; iii) -> FAST ; iv) -> YES ; v) -> NO ; vi) -> MAN
d. i) -> TOYOTA ; ii) -> RED ; iii) -> SLOW ; iv) -> NO ; v) -> NO ; vi) -> MAN
33. Select the correct features of Smart Bot: (CBSE 2022 – 2023)
a. Smart-bots are flexible and powerful
b. Coding is required to take this up on board
c. Smart bots work on bigger databases and other resources directly
d. All of the above
34. For _ the whole corpus is divided into sentences. Each sentence is taken as a different data so now the whole corpus gets reduced to sentences. (CBSE 2022 – 2023)
a. Text Regulation
b. Sentence Segmentation
c. Tokenisation
d. Stemming
35. _________________is the sub-field of AI that is focused on enabling computers to understand and process human languages.
a. Deep Learning
b. Machine Learning
c. NLP
d. Data Sciences
36. In___________________, the machine is trained with huge amounts of data which helps it in training itself around the data.
a. Supervised Learning
b. Deep Learning
c. Classification
d. Unsupervised Learning
37. With reference to NLP, consider the following plot of occurrence of words versus their value: (CBSE 2022 – 2023)
In the given graph, X represents:
a. Rare/valuable words
b. Punctuation words
c. Popular words
d. Pronoun
38. Which of the following is a feature of document classification? (CBSE 2022 – 2023)
a. Helps in classifying the type and genre of a document.
b. Helps in creating a document.
c. Helps to display important information of a corpus.
d. Helps in including the necessary words in the text body.
39. _ is an example of Applications of Natural Language Processing.
a. Evaluation
b. Automatic Summarization
c. Deep Learning
d. Problem Scoping
40. Give one example of an application which uses augmented reality.
Show Answer ⟶41. Bag of Words is a model which helps in extracting features out of the text which can be helpful in machine learning algorithms. (CBSE 2023 – 2024)
a. Data Science (DS)
b. Virtual Reality (VR)
c. Natural Language Processing (NLP)
d. Computer Vision (CV)
42. Which domain of AI is used for interacting with virtual assistants such as Siri and Alexa? (CBSE 2023 – 2024)
a. Machine Learning (ML)
b. Computer Vision (CV)
c. Natural Language Processing (NLP)
d. Technical Vision (TV)
43. Two popular examples of pocket assistants are and _. (CBSE 2022 – 2023)
Show Answer ⟶44. Smart Assistants such as Alexa, Siri are the examples of: (CBSE 2022 – 2023)
a. Natural Language Processing
b. Data Science
c. Machine Learning
d. Computer Vision
45. Email filters, spam filters, smart assistants are the examples of: (CBSE 2022 – 2023)
a. Pocket Assistants
b. CV
c. NLP
d. Evaluation
46. NLP stands for _________.
a. Natural Language Processing
b. Nature Language Processing
c. None Language Processing
d. None of the above
47. ___________, is the sub-field of AI that is focused on enabling computers to understand and process human languages.
a. Natural Language Processing
b. Data Science
c. Computer Vision
d. None of the above
48. __________ is the sub-field of AI that make the interactions between computers and human (natural) languages
a. Natural Language Processing
b. Data Science
c. Computer Vision
d. None of the above
49. Which of the games below is related to natural language processing?
a. Voice Assistants
b. Chatbots
c. Mystery Animal
d. Grammar Checkers
50. Applications of Natural Language Processing
a. Automatic Summarization
b. Sentiment Analysis
c. Text Classification
d. All of the above
51. ___________ Information overload is a real problem when we need to access a specific, important piece of information from a huge knowledge base.
a. Automatic Summarization
b. Sentiment Analysis
c. Text Classification
d. All of the above
52. ___________ is especially 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.
a. Automatic Summarization
b. Sentiment Analysis
c. Text Classification
d. All of the above
53. The goal of sentiment analysis is to identify sentiment among several posts or even in the same post where emotion is not always explicitly expressed.
a. Automatic Summarization
b. Sentiment Analysis
c. Text Classification
d. All of the above
54. Companies use Natural Language Processing applications, such as _________, to identify opinions and sentiment online to help them understand what customers think about their products and services.
a. Automatic Summarization
b. Sentiment Analysis
c. Text Classification
d. All of the above
55. ___________ 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. Automatic Summarization
b. Sentiment Analysis
c. Text Classification
d. All of the above
56. __________ device helps to communicate with humans and abilities to make humans lives easier.
a. Google Assistant
b. Cortana
c. Siri
d. All of the above
57. _____________ 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
58. By dividing up large problems into smaller ones, ____________ aims to help you manage them in a more constructive manner.
a. CDP
b. CBT
c. CSP
d. CLP
59. CBT stands for ____________.
a. Common Behavioural Therapy (CBT)
b. Cognitive Behavioural Therapy (CBT)
c. Connection Behavioural Therapy (CBT)
d. None of the above
60. Cognitive behavioural Therapy includes __________.
a. Your Thoughts
b. Your Behaviors
c. Your Emotions
d. All of the above
61. ________ 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. Common Behavioural Therapy (CBT)
b. Cognitive Behavioural Therapy (CBT)
c. Connection Behavioural Therapy (CBT)
d. None of the above
62. ____________ by collecting data from various reliable and authentic sources.
a. Data Acquisition
b. Database
c. Data Mining
d. None of the above
63. Once the textual data has been collected, it needs to be processed and cleaned so that an easier version can be sent to the machine. This is known as __________.
a. Data Acquisition
b. Data Exploration
c. Data Mining
d. None of the above
64. Once the text has been normalized, it is then fed to an NLP based AI model. Note that in NLP, modelling requires data pre-processing only after which the data is fed to the machine.
a. Data Acquisition
b. Modelling
c. Data Mining
d. None of the above
65. The model trained is then evaluated and the accuracy for the same is generated on the basis of the
relevance of the answers which the machine gives to the user’s responses.
a. Data Acquisition
b. Modelling
c. Evaluation
d. None of the above
66. One of the most common applications of Natural Language Processing is a chatbot, give some examples of chatbots __________.
a. Mitsuku Bot
b. CleverBot
c. Jabberwacky
d. All of the above
67. There are ______ different types of chatbots.
a. 2
b. 3
c. 4
d. 5
68. Which of the following is related to chatbots.
a. Script-bot
b. Smart-bot
c. Both a) and b)
d. None of the above
69. _______ bots work around a script which is programmed in them.
a. Script-bot
b. Smart-bot
c. Both a) and b)
d. None of the above
70. ________ work on bigger databases and other resources directly.
a. Script-bot
b. Smart-bot
c. Both a) and b)
d. None of the above
71. ___________ 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. Speech Normalization
b. Text Normalization
c. Visual Normalization
d. None of the above
72. ________ the whole corpus is divided into sentences. Each sentence is taken as a different data so now the whole corpus gets reduced to sentences.
a. Sentence Normalization
b. Sentence Segmentation
c. Sentence Tokenization
d. All of the above
73. Under __________, every word, number and special character is considered separately and each of them is now a separate token.
a. Tokenization
b. Token normalization
c. Token segmentation
d. All of the above
74. In Tokenization each sentence is divided into _________.
a. Block
b. Tokens
c. Parts
d. None of the above
75. __________ are the words which occur very frequently in the corpus but do not add any value to it.
a. Tokens
b. Words
c. Stopwords
d. None of the above
76. Stopwords are the words which occur very frequently in the corpus but do not add any value to it. for example_________.
a. Grammatical words
b. Simple words
c. Complex words
d. All of the above
77. Give the example of stop words __________.
a. an
b. and
c. are
d. All of the above
78. The machine does not consider ___________words as same words because of different cases.
a. Upper case
b. Lower case
c. Case sensitivity
d. None of the above
79. ___________ is the process in which the affixes of words are removed and the words are converted to their base form.
a. Stemming
b. Stopwords
c. Case-sensitivity
d. All of the above
80. Stemming and lemmatization both are _________ processes.
a. Same process
b. Alternative process
c. Other process
d. All of the above
81. ________ makes sure that lemma is a word with meaning and hence it takes a longer time to execute than stemming.
a. Stopwords
b. Stemming
c. Lemmatization
d. Token normalization
82. ___________ is a Natural Language Processing model which helps in extracting features out of the text which can be helpful in machine learning algorithms.
a. Bag of Words
b. Big Words
c. Best Words
d. All of the above
83. Which steps we have to approach to implement the bag of words algorithm.
a. Text Normalization
b. Create Dictionary
c. Create Document Vectors
d. All of the above
84. ________ identify each document in the corpus, find out how many times the word from the unique list of words has occurred.
a. Text Normalization
b. Create Dictionary
c. Document Vectors
d. All of the above
85. TFIDF stands for ___________.
a. Team Frequency and Inverse Document Frequency
b. Term Frequency and Inverse Document Frequency
c. Top Frequency and Inverse Document Frequency
d. Table Frequency and Inverse Document Frequency
86. Applications of TFIDF are ___________.
a. Document Classification
b. Topic Modelling
c. Information Retrieval System and Stop word filtering
d. All of the above
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