NATURAL LANGUAGE PROCESSING Subject Code: CS525PEbiet.ac.in/coursecontent/cse/threeone/NATURAL LANGUAGE...Natural Language Processing and Information Retrieval: Tanvier Siddiqui, U.S

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

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,



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



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


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


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


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


Issues

24

Models for Ambiguity

Resolution in


Issues

Analyze the Ambiguity

Resolution in


Issues

Unit – 3

25

7

Semantic Parsing:

Introduction

Understand the

semantic parsing


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


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


47 Meaning

Representation Systems Analyze the Meaning


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


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


II.Long Answer Questions-


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-


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



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


UNIT-3

I.Short Answer Questions-


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



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


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



Taxonomy Level

Course Outcome


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-


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-


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


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


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


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



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


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


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


View Answer


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).


UNIT-4


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


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


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 ?


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


1. NLP stands for Natural Language Processing.

a. True

b.False

View Answer

true


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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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


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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