natural language processing subject code: cs525pebiet.ac.in/coursecontent/cse/threeone/natural...
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NATURAL LANGUAGE PROCESSING
Subject Code: CS525PE
Regulations : R18 - JNTUH
Class: III Year B.Tech CSE I Semester
Department of Computer Science and Engineering
Bharat Institute of Engineering and Technology
Ibrahimpatnam-501510,Hyderabad
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CSE III Yr- I SEM 100
Natural Language Processing (CS525PE)
B.TECH III YEAR SEM-I
COURSE PLANNER
I. COURSE AIM:
The aim of this course is to have a comprehensive perspective of inclusive learning,
ability to learn and implement Natural Language Processing.
II. Course Objectives
1. Introduce to some of the problems and solutions of NLP and their relation to
linguistics and statistics.
III. COURSE OUTCOME: S.N
o
Description
Bloom’s Taxonomy Level
1 Able to Show sensitivity to linguistic phenomena and an ability to model
them with formal grammars. L1: REMEMBERING
2 Understand and carry out proper experimental methodology for training
and evaluating empirical NLP systems L2:UNDERSTANDING
3 Able to determine probabilities, construct statistical models over strings
and trees, and estimate parameters using supervised and unsupervised
training methods. L5: EVALUATING
4 Able to design, implement, and analyze NLP algorithms L6: CREATE
5 Able to design different language modeling Techniques. L6: CREATE
IV.HOW PROGRAM OUTCOMES ARE ASSESSED:
Program Outcomes (PO) Level
Proficiency
assessed
by
PO1 Engineering knowledge: Apply the knowledge of
mathematics, science, engineering fundamentals, and an
engineering specialization to the solution of complex
engineering problems related to Computer Science and
Engineering.
3 Assignments
PO2 Problem analysis: Identify, formulate, review research
literature, and analyze complex engineering
problems related to Computer Science and
Engineering and reaching substantiated conclusions
using first principles of mathematics, natural
sciences, and engineering sciences.
2
Assignments,
Tutorials,
Mock
Tests
PO3 Design/development of solutions: Design solutions for 2.5 Assignments,
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CSE III Yr- I SEM 101
Program Outcomes (PO) Level
Proficiency
assessed
by
complex engineering problems related to Computer
Science and Engineering and design system
components or processes that meet the specified
needs with appropriate consideration for the public
health and safety, and the cultural, societal, and
environmental considerations.
Tutorials,
Mock
Tests
PO4 Conduct investigations of complex problems: Use
research-based knowledge and research methods
including design of experiments, analysis and
interpretation of data, and synthesis of the
information to provide valid conclusions.
2.5 Assignments
PO5 Modern tool usage: Create, select, and apply
appropriate techniques, resources, and modern
engineering and IT tools including prediction and
modeling to complex engineering activities with an
understanding of the limitations.
2
Assignments,
Tutorials,
Mock
Tests
PO6 The engineer and society: Apply reasoning informed
by the contextual knowledge to assess societal,
health, safety, legal and cultural issues and the
consequent responsibilities relevant to the Computer
Science and Engineering professional engineering
practice.
3
Assignments,
Tutorials,
Mock
Tests
PO7 Environment and sustainability: Understand the
impact of the Computer Science and Engineering
professional engineering solutions in societal and
environmental contexts, and demonstrate the
knowledge of, and need for sustainable
development.
1 Assignments
PO8 Ethics: Apply ethical principles and commit to
professional ethics and responsibilities and norms of
the engineering practice.
- --
PO9 Individual and team work: Function effectively as an
individual, and as a member or leader in diverse
teams, and in multidisciplinary settings.
-
Assignments,
Tutorials,
Mock
Tests
PO10 Communication: Communicate effectively on complex
engineering activities with the engineering
community and with society at large, such as, being
able to comprehend and write effective reports and
design documentation, make effective presentations,
and give and receive clear instructions.
- --
PO11 Project management and finance: Demonstrate
knowledge and understanding of the engineering
and management principles and apply these to one‟s
own work, as a member and leader in a team, to
manage projects and in multidisciplinary
3
Assignments,
Tutorials,
Mock
Tests
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CSE III Yr- I SEM 102
Program Outcomes (PO) Level
Proficiency
assessed
by
environments.
PO12 Life-long learning: Recognize the need for, and have
the preparation and ability to engage in independent
and life-long learning in the broadest context of
technological change.
2 Assignments,
Tutorials
1: Slight (Low) 2: Moderate (Medium) 3: Substantial
(High) - : None
Program Specific Outcomes (PSO) Level Proficiency assessed by
PSO1 Foundation of mathematical concepts: To use mathematical Methodologies to crack problem using suitable mathematical analysis, data structure and suitable algorithm.
2.8
Lectures,
Assignments,
Tutorials, Mock
Tests PSO2 Foundation of Computer System:
The ability to interpret the fundamentalconcepts and methodology of computer systems. Students can understand the functionality of hardware and software aspects of computer systems.
2
Lectures,
Assignments,
Tutorials, Mock
Tests
PSO3 Foundations of Software development: The ability to grasp the software development lifecycle and methodologies of software systems. Possess competent skills and knowledge of software design process. Familiarity and practical proficiency with a broad area of programming concepts and provide new ideas and innovations towards research.
2.4
Lectures,
Assignments,
Tutorials, Mock
Tests
1: Slight (Low) 2: Moderate (Medium) 3: Substantial (High) None
JNTU SYLLABUS
UNIT - I
Finding the Structure of Words:
Words and Their Components, Issues and Challenges,Morphological Models
Finding the Structure of Documents: Introduction, Methods, Complexity of the
Approaches, Performances of the Approaches
UNIT - II
Syntax Analysis: Parsing Natural Language, Treebanks: A Data-Driven Approach
to Syntax, Representation of Syntactic Structure, Parsing Algorithms, Models for
Ambiguity Resolution in Parsing, Multilingual Issues
UNIT - III
Semantic Parsing: Introduction, Semantic Interpretation, System Paradigms, Word
Sense Systems, Software.
UNIT - IV
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CSE III Yr- I SEM 103
Predicate-Argument Structure, Meaning Representation Systems, Software.
UNIT - V
Discourse Processing: Cohension, Reference Resolution, Discourse Cohension
and Structure
Language Modeling: Introduction, N-Gram Models, Language Model Evaluation, Parameter
Estimation, Language Model Adaptation, Types of Language Models, Language-Specific
Modeling Problems, Multilingual and Crosslingual Language Modeling
TEXT BOOKS:
1. Multilingual natural Language Processing Applications: From Theory to Practice – Daniel M.
Bikel and Imed Zitouni, Pearson Publication
2. Natural Language Processing and Information Retrieval: Tanvier Siddiqui, U.S. Tiwary
REFERENCE:
1. Speech and Natural Language Processing - Daniel Jurafsky & James H Martin, Pearson
Publications
LESSON PLAN-COURSE SCHEDULE:
S.N
o Wee
k Topics
Course Learning
Outcomes Teaching
Methodologies Text
Book
Unit – 1
1
1
Object Based
Education(OBE)Orient
ation
Understand OBE
Black Board & PPT T1
2 Finding the Structure
of Words: Words and
Their Components
Understand the
Structure of Word and
components
3 Words and Their Components
Understand the
Structure of Word and
components
4 Issues and Challenges,
Understand the issues
and challenges in words
5
2
Morphological Models
Analyze the
morphological Models
6
Morphological Models
Analyze the
morphological Models
7 Finding the Structure
of Documents:
Introduction,
Understand the
Documents
8 Methods Understand Methods
9
3
Methods Understand Methods
10 Complexity of the
Approaches Analyze the Models
complexity
11 Complexity of the
Approaches Analyze the Models
complexity
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CSE III Yr- I SEM 104
12 Performances of the Approaches
Remember the
performance of the
Models
13 Mock Test #1
Unit – 2
14
4
Syntax Analysis:
Parsing Natural
Language Understand the Syntax
analysis
Black Board & PPT T1
15 Parsing Natural Language
Define the Parsing of
Natural Language
16 Bridge Class #1
17 Treebanks: A Data-
Driven Approach to
Syntax
Analyze the Tree Banks
approach
18
5
Treebanks: A Data-
Driven Approach to
Syntax
Analyze the Tree Banks
approach
19 Representation of Syntactic Structure
Understand the
representation of
Syntactic Structure
20 Bridge Class #2
21 Parsing Algorithms
Understand the Parsing
Algorithms
22
6
Parsing Algorithms
Understand the Parsing
Algorithms
23
Models for Ambiguity
Resolution in
Parsing,Multilingual
Issues
Analyze the Ambiguity
Resolution in
Parsing,Multilingual
Issues
24
Models for Ambiguity
Resolution in
Parsing,Multilingual
Issues
Analyze the Ambiguity
Resolution in
Parsing,Multilingual
Issues
Unit – 3
25
7
Semantic Parsing:
Introduction
Understand the
semantic parsing
Black Board & PPT T1
26
27
Semantic Interpretation
Analyze the semantic
Interpretation 28
29
8
Semantic Interpretation
Analyze the semantic
Interpretation 30
31 ** NLP Programming Using Python
Design the coding part
for the NLP
32 Bridge Class #3
33
9
System Paradigms
Understand the System
Paradigms Black Board & PPT T1
34
35
System Paradigms
Understand the System
Paradigms 36
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CSE III Yr- I SEM 105
37
10
Word Sense Systems
Understand the Word
sense Systems
38 Word Sense Systems
Understand the Word
sense Systems
39 Software related to word sense
Understand the software
which used in NLP
Unit – 4
40
11
Predicate Understand the
predicate logic
Black Board & PPT T1 & T2
41 Argument Structure
Understand the
Argument structure
details
42 Argument Structure Understand the
Argument structure
details
43 Argument Structure Understand the
Argument structure
details
44
12
Seminars by students
45 Meaning
Representation
Systems,
Analyze the Meaning
representation systems
46 Meaning
Representation Systems Analyze the Meaning
representation systems
47 Meaning
Representation Systems Analyze the Meaning
representation systems
48
13
Software for
representation
mechanism
Understand the
software
49 ** NDLK Tool Kit
Understand The tool
kits
50 ** NDLK Tool Kit
Understand The tool
kits
51 Bridge Class #4
52 14 Mock Test #2
Unit – 5
53
14
Discourse Processing:
Cohension, Reference
Resolution
Understand the
Cohension and reference
resolution
Black Board & PPT T1 & T2
54 Discourse Cohension
and Structure
Understand the
Discourse Cohension
and structure 55
56
15
Language Modeling:
Introduction
Understand the
Language Modeling
57 N-Gram Models
Analyze the N-Gram
Models
58 Language Model Evaluation,
Determine the language
model evaluation
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CSE III Yr- I SEM 106
59 Parameter Estimation
Analyze the parameter
Estimation
60
16
Language Model
Adaptation, Types of
Language Models,
Analyze the Language
Model Adaptation,
Types of Language
Models,
61 Language-Specific Modeling
Illustrate the Language-
Specific Modeling
62 Problems, Multilingual
and Crosslingual
Language Modeling
Problems, Multilingual
and Crosslingual
Language Modeling
63 Bridge Class #5
IX.MAPPING COURSE OUTCOMES LEADING TO THE ACHIEVEMENT
OF PROGRAM OUTCOMES AND PROGRAM SPECIFIC OUTCOMES:
Cou
rse
Ou
tcom
es
Program Outcomes (PO)
Program
Specific
Outcomes
(PSO)
PO1 PO2 PO3 PO4 PO5 PO6 PO7 PO8 PO9 PO10 PO11 PO12 PSO1 PSO2 PSO
3
CO1 - 2 3 3 3 - - - - - 3 2 3 2 3
CO2 - 2 2 2 1 - - - - - 3 2 3 2 2
CO3 - 2 3 3 3 3 - - - - 3 2 3 2 2
CO4 - - 2 2 1 - - - - - 3 2 3 2 2
CO5 3 - - - 2 3 1 - - - - - 2 2 3
AV
G 3 2 2.5 2.5 2 3 1 - - - 3 2 2.8 2 2.4
3: Substantial (High) - : None
1: Slight
(Low)
2: Moderate
(Medium)
QUESTION BANK: (JNTUH)
UNIT-I
I. Short Answer Questions-
S.No Question Blooms
Taxonomy Level
Course Outcome
1 List the methods of Word components
L1 1
2 Define NLP L1 1
3 What is Natural Language Processing? Discuss
with some applications.
L1 1
4 Analyze the usage of feature structures in NLP. L1 2
5 What do you meant by NLP algorithm L1 2
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CSE III Yr- I SEM 107
II.Long Answer Questions-
S.No Question Blooms
Taxonomy Level
Course Outco
me
1 Design a finite state transducer with E-insertion
orthographic rule that parses
from surface level “foxes” to lexical level
“fox+N+PL” using FST.
L5 2
2 Analyse how statistical methods can be used in
machine translation
L4 3
3 Explain the complexity approaches L2 3
4 Explain the Performances analysis L1 2
5 Explain the structure documents L1 1
UNIT-2
I.Short Answer Questions-
S.No Question Blooms
Taxonomy Level
Course Outcom
e
1 Define Parsing
L1 2
2 What is Treebanlk? L1 3
3 Define Syntax L2 3
4 List the parsing algorithms L1 3
5 Define Multilingual L2 3
II.Long Answer Questions-
S.No Question Blooms
Taxonomy Level
Course
Outcome
1 Explain the parsing of NLP
L1 2
2 Explain the Tree Bank method with example L2 3
3 Explain data –driven mechanism L3 3
4 Explain the models of ambiguity resolution L1 3
5 Explain the Multilingual issues L2 3
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CSE III Yr- I SEM 108
UNIT-3
I.Short Answer Questions-
S.No Question Blooms
Taxonomy Level
Course
Outcome
1 Define semantic
L1 2
2 List the semantic rules L2 3
3 Define system paradigm L1 3
4 What is word sense system L2 3
II.Long Answer Questions-
S.No Question Blooms
Taxonomy Level
Course
Outcome
1 Explain in detail about semantic interpretation.
L2 5
2 Explain System paradigms L1 5
3 Explain the methods of word sense systems L2 5
4 Explain the software‟s associated with sematic
interpretation
L4 5
UNIT-4
I.Short Answer Questions
S.No Question Blooms
Taxonomy Level
Course Outcome
1 Define Predicate Logic
L1 5
2 Give example for predicate logic L1 5
3 Define argument structure L2 5
4 Define structure management L2 5
5 Define representation in NLP L3 5
II.Long Answer Questions-
S.No Question Blooms
Taxonomy Level
Course Outcome
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CSE III Yr- I SEM 109
1 Explain in detail about predicate logic with
examples. L1 5
2 Explain in detail about argument structure in
NLP
L2 5
3 Explain in detail about meaning representation
system
L2 5
4 List and explain the meaning representation L2 5
UNIT-5
I. Short Answer Questions-
S.No Question Blooms
Taxonomy Level
Course Outcome
1 Define cohension
L1 5
2 Define reference resolution L2 5
3 Define discourse cohension L1 5
4 Define modeling L2 5
5 What do you meant by crosslingual L3 5
. II. Long Answer Questions-
S.No Question Blooms
Taxonomy Level
Course Outcome
1 Explain in detail about reference resolution
L1 5
2 Explain in detail about discourse of cohesion L1 5
3 Explain in detail about N- Gram Models L3 5
4 Explain in detail about language specific models L2 5
5 Discuss about language model adaptation L4 5
TEXT BOOKS:
1. Multilingual natural Language Processing Applications: From Theory to Practice – Daniel M.
Bikel and Imed Zitouni, Pearson Publication
2. Natural Language Processing and Information Retrieval: Tanvier Siddiqui, U.S. Tiwary
REFERENCE:
1. Speech and Natural Language Processing - Daniel Jurafsky & James H Martin, Pearson
Publications
MCQ Questions
Unit – 1
1. What is the field of Natural Language Processing (NLP)?
a) Computer Science
b) Artificial Intelligence
c) Linguistics
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CSE III Yr- I SEM 110
d) All of the mentioned
Answer: d
Explanation: None.
2. NLP is concerned with the interactions between computers and human (natural) languages.
a) True
b) False
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
Answer: a
Explanation: There are enormous ambiguity exists when processing natural language.
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
Answer: d
Explanation: None.
FILL IN THE BLANKS:
6. Which includes major tasks of NLP? Automatic Summarization
7. What is Coreference Resolution?
Given a sentence or larger chunk of text, determine which words (“mentions”) refer to the
same objects (“entities”)
8. What is Machine Translation?Converts one human language to another
9. The more general task of coreference resolution also includes identifying so-called
“bridging relationships” involving referring expressions.
10. What is Morphological Segmentation?
Separate words into individual morphemes and identify the class of the morphemes
UNIT-2
MULTIPLE CHOICE QUESTIONS:
1. Select a Machine Independent phase of the compiler a) Syntax Analysis
b) Intermediate Code generation
c) Lexical Analysis
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CSE III Yr- I SEM 111
d) All of the mentioned
View Answer
Answer: d
Explanation: All of them work independent of a machine.
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2. A system program that combines the separately compiled modules of a program into a form
suitable for execution?
a) Assembler
b) Compiler
c) Linking Loader
d) Interpreter
View Answer
Answer: c
Explanation: A loader which combines the functions of a relocating loader with the ability to
combine a number of program segments that have been independently compiled.
3. Which of the following system software resides in the main memory always
a) Text Editor
b) Assembler
c) Linker
d) Loader
View Answer
Answer: d
Explanation: Loader is used to loading programs.
4. Output file of Lex is _____ the input file is Myfile?
a) Myfile.e
b) Myfile.yy.c
c) Myfile.lex
d) Myfile.obj
View Answer
Answer: b
Explanation: This Produce the filr “myfile.yy.c” which we can then compile with g++.
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5. Type checking is normally done during?
a) Lexical Analysis
b) Syntax Analysis
c) Syntax Directed Translation
d) Code generation
View Answer
Answer: c
Explanation: It is the function of Syntax directed translation.
FILL IN THE BLANKS:
6. Suppose One of the Operand is String and other is Integer then it does not throw error as it
only checks whether there are two operands associated with „+‟ or not .
7. In Short Syntax Analysis Generates Parse Tree
8. By whom is the symbol table created?Compiler
9. What does a Syntactic Analyser do?Create parse tree
10. Semantic Analyser is used for?Generating Object code & Maintaining symbol table
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CSE III Yr- I SEM 112
UNIT-3
1. Which of the following is the fastest logic ?
a) TTL
b) ECL
c) CMOS
d) LSI
View Answer
Answer: b
Explanation: In electronics, emitter-coupled logic (ECL) is a high-speed integrated circuit.
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2. A bottom up parser generates
a) Right most derivation
b) Rightmost derivation in reverse
c) Leftmost derivation
d) Leftmost derivation in reverse
View Answer
Answer: b
Explanation: This corresponds to starting at the leaves of the parse tree also known as shift-
reduce parsing.
3. A grammar that produces more than one parse tree for some sentence is called
a) Ambiguous
b) Unambiguous
c) Regular
d) None of the mentioned
View Answer
Answer: a
Explanation: ambiguous grammar has more than one parse tree.
4. An optimizer Compiler
a) Is optimized to occupy less space
b) Both of the mentioned
c) Optimize the code
d) None of the mentioned
View Answer
Answer: d
Explanation: In computing, an optimizing compiler is a compiler that tries to minimize or
maximize some attributes of an executable computer program.
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5. The linker
a) Is similar to interpreter
b) Uses source code as its input
c) I s required to create a load module
d) None of the mentioned
View Answer
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CSE III Yr- I SEM 113
Answer: c
Explanation: It is a program that takes one or more object files generated by a compiler and
combines them into a single executable file, library file, or another object file.
FILL IN THE BLANKS:
6. A latch is constructed using two cross coupled NAND gates
7. Pee Hole optimization Constant folding
8. The optimization which avoids test at every iteration is Loop unrolling
9. Scissoring enables A part of data to be displayed
10. Shift reduce parsers are Bottom Up parser
1. Given a stream of text, Named Entity Recognition determines which pronoun maps to which
noun.
a) False
b) True
Answer: a
Explanation: Given a stream of text, Named Entity Recognition determines which items in
the text maps to proper names.
2. Natural Language generation is the main task of Natural language processing.
a) True
b) False
Answer: a
Explanation: Natural Language Generation is to Convert information from computer
databases into readable human language.
3. OCR (Optical Character Recognition) uses NLP.
a) True
b) False
Answer: a
Explanation: Given an image representing printed text, determines the corresponding text.
4. 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
Answer: d
Explanation: A Bayesian network provides a complete description of the domain.
5. Parsing determines Parse Trees (Grammatical Analysis) for a given sentence.
a) True
b) False
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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CSE III Yr- I SEM 114
UNIT-4
MULTIPLE CHOICE QUESTIONS:
1. IR (information Retrieval) and IE (Information Extraction) are the two same thing.
a) True
b) False
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.
2. 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
Answer: b
Explanation: Shallow Semantic Analysis doesn‟t cover word sense disambiguation.
3. 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
Answer: b
Explanation: NLP is required to linguistic analysis.
4. Speech Segmentation is a subtask of Speech Recognition.
a) True
b) False
Answer: a
Explanation: None.
5. 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
Answer: b
FILL IN THE BLANKS:
6. Which of these is also known as look-head LR parser? LLR
7. What is the similarity between LR, LALR and SLR? Use same algorithm, but different
parsing table
8. An LR-parser can detect a syntactic error as soon as It is possible to do so a left-to-right
scan of the input
9. Which of these is true about LR parsing ?
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CSE III Yr- I SEM 115
Is most general non-backtracking shift-reduce parsing and It is still efficient
10. If a state does not know whether it will make a shift operation or reduction for a terminal
is called Shift/reduce conflict
UNIT-5
MULTIPLE CHOICE QUESTIONS:
1. NLP stands for Natural Language Processing.
a. True
b.False
View Answer
true
2. NLP is concerned with the interactions between computers and human (natural) languages.
a.yes
b.no
View Answer
Yes
3. The following areas where NLP can be useful -
Automatic Text Summarization
Information Retrieval
Automatic Question-Answering Systems
All of the Above
View Answer
All of the above
4. Machine Translation is that converts -
Human language to machine language
One human language to another
Any human language to English
Machine language to human language
View Answer
One human language to another
5. Which of the following is the field of Natural Language Processing (NLP)?
Computer Science
Artificial Intelligence
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CSE III Yr- I SEM 116
Computational linguistics
All of the above
View Answer
All of the above
FILL IN THE BLANKS:
6. What is Natural Language Processing good for? Summarize blocks of text
7. You can build a machine learning RSS reader in less than 30-minutes using - ScrapeRSS
8. Natural Language Processing (NLP) is the field of Computer Science
9. NLP is concerned with the interactions between computers and human (natural) languages.
10. One of the main challenge/s of NLP Is Handling Ambiguity of Sentences
JOURNALS:1. Natural Language Processing Research, ISSN: 2666 – 0512
2. Journal of Information : Special Issues on NLP, ISSN : 2078 - 2489
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