Natural Language Processing
UNIT 1
Introduction to NLP: NLP – introduction and applications, NLP phases, Difficulty of NLP including ambiguity; Spelling error and Noisy Channel Model; Concepts of Parts-of-speech and Formal Grammar of English
Important Questions
1. Explain the advantages and disadvantaged of NLP
2. Analyse the Applications of NLP
3. Explain different phases of NLP
4. Explain Spelling error and Noisy Channel Model in NLP
5. Explain Concepts of Parts-of-speech and Formal Grammar of English
UNIT 2
Language Modelling: N-gram and Neural Language Models Language Modelling with N-gram, Simple N-gram models, Smoothing (basic techniques),Evaluating language models; Neural Network basics, Training; Neural Language Model, Case study: application of neural language model in NLP system development.
Important Questions
1. Explain different Neural Language Models
2. Explain Language Modelling with N-gram
3. Explain metrics for language modeling
4. Explain evaluating language models
5. Explain the case study of APPLICATION OF NEURAL LANGUAGE MODEL IN NLP SYSTEM DEVELOPMENT
UNIT-3
Parts-of-speech Tagging Parts-of-speech Tagging: basic concepts; Tagset; Early approaches: Rule based and TBL; POS tagging using HMM, Introduction to POS Tagging using Neural Model.
Important Questions
1. Explain the basic concepts of Parts-of-speech Tagging
2. Explain Rule based and TBL
3. Explain POS tagging using HMM
4. Explain POS Tagging using Neural Model
MCQ'S
NATURAL LANGUAGE PROCESSING
MULTIPLE CHOICE QUESTIONS
1. What is the field of Natural Language Processing (NLP)?
a) Computer Science
b) Artificial Intelligence
c) Linguistics
d) All of the mentioned
View Answer
Answer: d
Explanation: None.
2. NLP is concerned with the interactions between computers and human (natural) languages.
a) True
b) False
View Answer
Answer: a
Explanation: NLP has its focus on understanding the human spoken/written language and converts that interpretation into machine understandable language.
3. What is the main challenge/s of NLP?
a) Handling Ambiguity of Sentences
b) Handling Tokenization
c) Handling POS-Tagging
d) All of the mentioned
View Answer
Answer: a
Explanation: There are enormous ambiguity exists when processing natural language.
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4. Modern NLP algorithms are based on machine learning, especially statistical machine learning.
a) True
b) False
View Answer
Answer: a
Explanation: None.
5. Choose form the following areas where NLP can be useful.
a) Automatic Text Summarization
b) Automatic Question-Answering Systems
c) Information Retrieval
d) All of the mentioned
View Answer
Answer: d
Explanation: None.
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6. Which of the following includes major tasks of NLP?
a) Automatic Summarization
b) Discourse Analysis
c) Machine Translation
d) All of the mentioned
View Answer
Answer: d
Explanation: There is even bigger list of tasks of NLP. http://en.wikipedia.org/wiki/Natural_language_processing#Major_tasks_in_NLP.
7. What is Coreference Resolution?
a) Anaphora Resolution
b) Given a sentence or larger chunk of text, determine which words (“mentions”) refer to the same objects (“entities”)
c) All of the mentioned
d) None of the mentioned
View Answer
Answer: b
Explanation: Anaphora resolution is a specific type of coreference resolution.
8. What is Machine Translation?
a) Converts one human language to another
b) Converts human language to machine language
c) Converts any human language to English
d) Converts Machine language to human language
View Answer
Answer: a
Explanation: The best known example of machine translation is google translator.
9. The more general task of coreference resolution also includes identifying so-called “bridging relationships” involving referring expressions.
a) True
b) False
View Answer
Answer: a
Explanation: Refer the definition of Coreference Resolution.
10. What is Morphological Segmentation?
a) Does Discourse Analysis
b) Separate words into individual morphemes and identify the class of the morphemes
c) Is an extension of propositional logic
d) None of the mentioned
View Answer
Answer: b
Explanation: None.
11. Given a stream of text, Named Entity Recognition determines which pronoun maps to which noun.
a) False
b) True
View Answer
Answer: a
Explanation: Given a stream of text, Named Entity Recognition determines which items in the text maps to proper names.
12. Natural Language generation is the main task of Natural language processing.
a) True
b) False
View Answer
Answer: a
Explanation: Natural Language Generation is to Convert information from computer databases into readable human language.
13. OCR (Optical Character Recognition) uses NLP.
a) True
b) False
View Answer
Answer: a
Explanation: Given an image representing printed text, determines the corresponding text.
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14. Parts-of-Speech tagging determines ___________
a) part-of-speech for each word dynamically as per meaning of the sentence
b) part-of-speech for each word dynamically as per sentence structure
c) all part-of-speech for a specific word given as input
d) all of the mentioned
View Answer
Answer: d
Explanation: A Bayesian network provides a complete description of the domain.
15. Parsing determines Parse Trees (Grammatical Analysis) for a given sentence.
a) True
b) False
View Answer
Answer: a
Explanation: Determine the parse tree (grammatical analysis) of a given sentence. The grammar for natural languages is ambiguous and typical sentences have multiple possible analyses. In fact, perhaps surprisingly, for a typical sentence there may be thousands of potential parses (most of which will seem completely nonsensical to a human).
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16. IR (information Retrieval) and IE (Information Extraction) are the two same thing.
a) True
b) False
View Answer
Answer: b
Explanation: Information retrieval (IR) – This is concerned with storing, searching and retrieving information. It is a separate field within computer science (closer to databases), but IR relies on some NLP methods (for example, stemming). Some current research and applications seek to bridge the gap between IR and NLP.
Information extraction (IE) – This is concerned in general with the extraction of semantic information from text. This covers tasks such as named entity recognition, Coreference resolution, relationship extraction, etc.
17. Many words have more than one meaning; we have to select the meaning which makes the most sense in context. This can be resolved by ____________
a) Fuzzy Logic
b) Word Sense Disambiguation
c) Shallow Semantic Analysis
d) All of the mentioned
View Answer
Answer: b
Explanation: Shallow Semantic Analysis doesn’t cover word sense disambiguation.
18. Given a sound clip of a person or people speaking, determine the textual representation of the speech.
a) Text-to-speech
b) Speech-to-text
c) All of the mentioned
d) None of the mentioned
View Answer
Answer: b
Explanation: NLP is required to linguistic analysis.
19. Speech Segmentation is a subtask of Speech Recognition.
a) True
b) False
View Answer
Answer: a
Explanation: None.
20. In linguistic morphology _____________ is the process for reducing inflected words to their root form.
a) Rooting
b) Stemming
c) Text-Proofing
d) Both Rooting & Stemming
View Answer
Answer: b
Explanation: None.
21. NLP stands for Natural Language Processing.
A. true
B. false
ANS: A
22. NLP is concerned with the interactions between computers and human (natural) languages.
A. Yes
B. No
C. Not Sure
ANS: A
23. The following areas where NLP can be useful -
A. Automatic Text Summarization
B. Information Retrieval
C. Automatic Question-Answering Systems
D. All of the Above
ANS:D
24. Which of the following is the field of Natural Language Processing (NLP)?
A. Computer Science
B. Artificial Intelligence
C. Computational linguistics
D. All of the above
ANS: D
25. What is Natural Language Processing good for?
A.Summarize blocks of text
B.Automatically generate keyword tags
C.Identify the type of entity extracted
D.All of the above
ANS: D
26. You can build a machine learning RSS reader in less than 30-minutes using -
A. ScrapeRSS
B. Html2Text & AutoTag C. Sentiment Analysis
D. All of the mentioned
ANS: D
27. Natural Language Processing (NLP) is the field of
A.Artificial Intelligence
B.Computer Science
C.Linguistics
D.All of the above
ANS: B
28. NLP is concerned with the interactions between computers and human (natural) languages.
A. True
B. False
ANS:A
29. One of the main challenge/s of NLP Is _________________ .
A. Handling Tokenization
B. Handling Ambiguity of Sentences C. Handling POS-Tagging
D. All of the above
ANS: B
30. Choose form the following areas where NLP can be useful.
A.Automatic Text Summarization
B.Automatic Question-Answering Systems
C.Information Retrieval
D.All of the above
ANS: A
31. Coreference Resolution is -
A. Anaphora Resolution
B. Given a sentence or larger chunk of text, determine which words (“mentions”) refer to the same objects (“entities”)
C. Both a & b
D. None of the above
ANS: B
32. Morphological Segmentation
A.Is an extension of propositional logic
B.Does Discourse Analysis
C.Separate words into individual morphemes and identify the class of the morphemes
D.None of the mentioned
ANS: C
33. In linguistic morphology, _____________ is the process for reducing inflected words to their root form.
A. Stemming
B. Rooting
C. Text-Proofing
D. Both a & b
ANS: A
34. What is the name for information sent from robot sensors to robot controllers?
A. pressure
B. temperature
C. feedback
D. None of the above
ANS: C
35. What is the name for the space inside which a robot unit operates?
A. spatial base B. danger zone C. environment D. work envelop
ANS: D
36. Which is used to extract solution directly from the planning graph?
A. Hill-climbing search B. Planning algorithm C. Graphplan
D. None of the above
ANS:C
37. What is the starting level of planning graph?
A. Level 0 B. Level 1 C. Level 2 D. Level 3
ANS: A
38. Seymour Papert of the MIT AI lab created a programming environment for children called -
A. MYCIN B. LOGO
C. FORTRAN D. BASIC
ANS:B
39. Natural language processing is divided into the two subfields of -
A.symbolic and numeric
B.algorithmic and heuristic
C.time and motion
D.understanding and generation
ANS: D
40. The natural language is also known as .....................
A.3rd Generation language
B.4th Generation language
C.5th Generation language
D.6th Generation language
ANS: C
41. All of the following are challenges associated with natural language processing except
A.dividing up a text into individual words in English.
B.understanding the context in which something is said.
C.recognizing typographical or grammatical errors in texts
D.distinguishing between words that have more than one meaning.
ANS: A
42. Natural language processing (nlp) is associated with which of the following areas?
A.text mining
B.artificial intelligence
C.computational linguistics
D.All of the above
ANS: D
43. One example of a natural language programming software program used with the iphone is called siri.
A. True
B. False
ANS: A
44. What is the main challenges of NLP?
A. Handling Tokenization
B. Handling POS-Tagging
C. Handling Ambiguity of Sentences
B. D. None of the above
ANS: C
UNIT-4
Parsing Basic concepts: top down and bottom up parsing, treebank; Syntactic parsing: CKY parsing; Statistical parsing basics: Probabilistic Context Free Grammar (PCFG); Probabilistic CKY Parsing of PCFGs.
UNIT-5
Semantics Vector Semantics; Words and Vector; Measuring Similarity; Semantics with dense vectors; SVD and Latent Semantic Analysis; Embeddings from prediction: Skip-gram and CBOW; Concept of Word Sense; Introduction to WordNet.
Q.No. | Questions | Marks | |
1 | Unit-I | ||
a | Describe the Values and Principals of Agile Model | [14M] | |
OR | |||
b | Describe the different phases of any two Software development life cycle process models | [14M] | |
2 | Unit-II | ||
a | Analyse the DevOps with traditional process | [7M] | |
Analyse DevOps tools ecosystem | [7M] | ||
OR | |||
b | Analyse the phases of DevOps Pipeline | [7M] | |
Analyse the principles of DevOps and roles and responsibilities of DevOps Engineer | [7M] | ||
3 | Unit-III | ||
a | Describe the DevOps adoption in projects to focus on Technology aspects | [14M] | |
OR | |||
b | Describe the DevOps adoption in projects to focus on people and process aspects | [14M] | |
4 | Unit-IV | ||
a | Analyze the CI and CD Pipeline | [14M] | |
OR | |||
b | Analyse Benefits of CI/CD and Metrics to track CICD practices | [14M] | |
| Unit-V | ||
5 | a | Analyse the Key factors of DevOps maturity model | [14M] |
OR | |||
b | Analyse the different stages of DevOps maturity Model | [14M] | |
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