Artificial Intelligence For Engineering Quizzes Part 5
Q:1. The possible features of a text corpus in NLP
1.Count of the word
2.Identifying stop words
3.Predicting parts of Speech
4.All the above
Solution- 4.All the above
Reason 1- All of the above options are true.
Reason 2- the above options are true.
Q:2. Normalization techniques in NLP
a. Lemmatization
b. Bag of words
c. Stemming
d. Named entity recognition
1.a,b
2.a,c
3.d,b
4.b,c
Solution- 2. a,c
Reason 1- In NLP a highly overlooked preprocessing step is text normalization. and Lemmatization on the surface is very similar to stemming, where the goal is to remove inflections and map a word to its root form. So these two are the normalization techniques in NLP.
Reason 2- In NLP a highly overlooked preprocessing step is text normalization. and Lemmatization on the surface is extremely almost like stemming, where the goal is to get rid of inflections and map a word to its root form. So these two are the normalization techniques in NLP.
Q:3. NLP Use cases
a. Text summarization
b. Object detection
c. Sentiment analysis
d. Chatbots
1.b,c,d
2.a,b,d
3.a, c, d
4.a,b,c
Solution- 3. a, c, d
Reason 1- Text summarization, Sentiment analysis and chatbots all uses natural language processing.
Q:4. Speech recognition
1.It is a way of encoding and decoding signals
2.It is coupled with AI as deep learning models
3.Both acoustic modeling and language modeling are important parts of modern statistically-based speech recognition algorithms.
4.All the above
Solution- 4. All the above
Reason 1- This is because in speech recognition, encoding and decoding signals is done and it is also coupled with AI as a deep learning model. and both the acoustic modeling and language modeling are important parts of modern statistically-based speech recognition algorithms.
Reason 2- This is because in speech recognition, encoding and decoding signals is completed and it’s also including AI as a deep learning model. and both the acoustic modeling and language modeling are important parts of recent statistically-based speech recognition algorithms.
Q:5. Choose an incorrect statement in context of speech recognition
1.In 1952, three Bell Labs researchers built a system called “Audrey”
2.Modern general-purpose speech recognition systems are based on Hidden Markov Models
3.It can identify objects, people, places, and actions in images
4.None of the above
Solution 3- It can identify objects, people, places, and actions in images
Reason 1- This is because speech recognition do not identify objects, people, places, and actions in images. This identification is done by object recognition and face recognition.
Reason 2- This is because speech recognition don’t identify objects, people, places, and actions in images. This identification is completed by visual perception and face recognition.
Q:6. Natural Language Understanding (NLU)
a. It is the ability of machines to understand the human language
b. It is a branch of Natural Language Processing
c. Natural-language understanding is considered an AI-hard problem.
d. None of the above
1.a,b,c
2.a,c,d
3.b,a,d
4.b,c,d
Solution- 1.a,b,c
Reason 1- NLU is a branch of NLP and it is considered an AI hard problem. It is the ability of machines to understand the human language
Q:7. Speech recognition steps include
1.Feature extraction
2.Spectrum analysis
3.Preprocessing of input signals
4.All the above
Solution- 4. All the above
Reason 1- Speech recognition process takes place in three main steps which are acoustic processing, feature extraction and classification/recognition. It also include spectrum analysis and preprocessing of input signals.
Reason 2- Speech recognition process takes place in 3 main steps which are acoustic processing, feature extraction and classification/recognition. It also include spectrum and preprocessing of input signals.
Q:8. The interpretation capabilities of a language-understanding system depend on
1.The semantic Theory
2.The syntactic theory
3.Both a and b
4.None of the above
Solution- 1. The semantic Theory
Reason 1- The interpretation capabilities of a language-understanding system depend on the semantic theory it uses. Semantic parsers convert natural-language texts into formal meaning representations.
Q:9. Applications of NLU
a. Automated reasoning
b. Machine translation
c. Network congestion control
d. All the above
1.c,d
2.b,c
3.d,a
4.a,b
Solution- 4.a,b
Reason 1- Before a computer can process unstructured text into a machine-readable format, first machines need to understand the peculiarities of the human language. It gives machines a form of reasoning or logic, and allows them to infer new facts by deduction. Simply put, using previously gathered and analyzed information, computer programs are able to generate conclusions.
Reason 2- Before a computer can process unstructured text into a machine-readable format, first machines got to understand the peculiarities of the human language. It gives machines a sort of reasoning or logic, and allows them to infer new facts by deduction. Simply put, using previously gathered and analyzed information, computer programs are ready to generate conclusions.
Q:10. Methods used in speech recognition systems are
1.Hidden Markov Model (HMM)
2.Neural Networks
3.Both a and b
4.None of the above
Solution- 3. Both a and b
Reason 1- at CMU, Raj Reddy’s students James Baker and Janet M. Baker began using the Hidden Markov Model (HMM) for speech recognition. speech recognition was still dominated by traditional approaches such as Hidden Markov Models combined with feedforward artificial neural networks.
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