Teachers and Examiners (CBSESkillEduction) collaborated to create the Natural Language Processing Class 10 MCQ. All the important MCQs are taken from the NCERT Textbook Artificial Intelligence ( 417 ) class X.
Natural Language Processing Class 10 MCQ
1. NLP stands for _________.
a. Natural Language Processing
b. Nature Language Processing
c. None Language Processing
d. None of the above
2. ___________, 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
3. __________ 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
4. Which of the games below is related to natural language processing?
a. Voice Assistants
b. Chatbots
c. Mystery Animal
d. Grammar Checkers
5. Applications of Natural Language Processing
a. Automatic Summarization
b. Sentiment Analysis
c. Text Classification
d. All of the above
6. ___________ 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
7. ___________ 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
8. 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
9. 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
10. ___________ 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
11. __________ 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
12. _____________ 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
13. 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
14. CBT stands for ____________.
a. Common Behavioural Therapy (CBT)
b. Cognitive Behavioural Therapy (CBT)
c. Connection Behavioural Therapy (CBT)
d. None of the above
15. Cognitive behavioural Therapy includes __________.
a. Your Thoughts
b. Your Behaviors
c. Your Emotions
d. All of the above
16. ________ 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
17. ____________ by collecting data from various reliable and authentic sources.
a. Data Acquisition
b. Database
c. Data Mining
d. None of the above
18. 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
19. 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
20. 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
21. 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
22. There are ______ different types of chatbots.
a. 2
b. 3
c. 4
d. 5
23. Which of the following is related to chatbots.
a. Script-bot
b. Smart-bot
c. Both a) and b)
d. None of the above
24. _______ 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
25. ________ work on bigger databases and other resources directly.
a. Script-bot
b. Smart-bot
c. Both a) and b)
d. None of the above
26. ___________ 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
27. ________ 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
28. 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
29. In Tokenization each sentence is divided into _________.
a. Block
b. Tokens
c. Parts
d. None of the above
30. __________ 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
31. 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
32. Give the example of stop words __________.
a. an
b. and
c. are
d. All of the above
33. 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
34. ___________ 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
35. Stemming and lemmatization both are _________ processes.
a. Same process
b. Alternative process
c. Other process
d. All of the above
36. ________ 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
37. ___________ 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
38. 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
39. ________ 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
40. 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
41. Applications of TFIDF are ___________.
a. Document Classification
b. Topic Modelling
c. Information Retrieval System and Stop word filtering
d. All of the above
Employability skills Class 10 Notes
- Unit 1- Communication Skills Class 10 Notes
- Unit 2- Self-Management Skills Class 10 Notes
- Unit 3- Basic ICT Skills Class 10 Notes
- Unit 4- Entrepreneurial Skills Class 10 Notes
- Unit 5- Green Skills Class 10 Notes
Employability skills Class 10 MCQ
- Unit 1- Communication Skills Class 10 MCQ
- Unit 2- Self-Management Skills Class 10 MCQ
- Unit 3- Basic ICT Skills Class 10 MCQ
- Unit 4- Entrepreneurial Skills Class 10 MCQ
- Unit 5- Green Skills Class 10 MCQ
Employability skills Class 10 Questions and Answers
- Unit 1- Communication Skills Class 10 Questions and Answers
- Unit 2- Self-Management Skills Class 10 Questions and Answers
- Unit 3- Basic ICT Skills Class 10 Questions and Answers
- Unit 4- Entrepreneurial Skills Class 10 Questions and Answers
- Unit 5- Green Skills Class 10 Questions and Answers
Artificial Intelligence Class 10 Notes
- Unit 1 – Introduction to Artificial Intelligence Class 10 Notes
- Unit 2 – AI Project Cycle Class 10 Notes
- Unit 3 – Natural Language Processing Class 10 Notes
- Unit 4 – Evaluation Class 10 Notes
- Advanced Python Class 10 Notes
- Computer Vision Class 10 Notes
Artificial Intelligence Class 10 MCQ
- Unit 1 – Introduction to Artificial Intelligence Class 10 MCQ
- Unit 2 – AI Project Cycle Class 10 MCQ
- Unit 3 – Natural Language Processing Class 10 MCQ
- Unit 4 – Evaluation Class 10 MCQ