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1.
2.
3.
Name of Course/Module
Course Code
Status of Subject
4.
MQF Level/Stage
5.
Version
(state the date of the last Senate approval)
6.
Pre-Requisite
7.
Name(s) of academic/teaching staff
8.
Semester and Year offered
9.
Objective of the course/module in the programme :
Natural Language Processing
TNL3221
Specialisation Core for B.IT (Hons) Artificial
Intelligence
Bachelor Degree – MQF Level 6
Date of previous
version :
Date of current
version :
June 2012
June 2014
TAI2151 Artificial Intelligence Fundamentals
Ong Lee Yeng
Lee Chin Poo
Trimester 2, Year 3
To introduce students to the field of natural language processing. Students will learn the formal
descriptions of natural language (such as English), and to algorithms and data structures based on the
formal description, to build a small natural language processing systems by using the Prolog
programming language.
10.
Justification for including the subject in the program :
Natural language processing is a major component for building an artificial intelligence system. This
course will provide students with the fundamental techniques of natural language processing, an
understanding of the limits of those techniques and the current research issues. Students will be able
to evaluate various potential applications in natural language processing.
11.
12.
Subject Learning Outcomes :
Level
LO1
Explain the basic notation in natural language processing
Cognitive
2
LO2
Identify the issues encountered in natural language processing system
Cognitive
4
LO3
Analyse the syntactical structure and semantic of sentences
Cognitive
4
LO4
Design a simple natural language processing system
Cognitive
5
Mapping of Learning Outcomes to Programme Outcomes :
Learning
PO1
PO2
PO3
PO4
PO5
Outcomes
LO1
X
LO2
PO6
LO3
LO4
Percentage
13.
Domain
PO7
PO8
PO9
X
X
X
X
X
X
X
25.00
37.50
37.50
Assessment Methods and Types :
Method and Type
Test
Description/Details
Percentage
20.00%
14.
Assignment
Report & Presentation
20.00%
Final Exam
Structured Questions
60.00%
Mapping of Assessment Components to Learning Outcomes:
Assessment Components
%
LO1
LO2
LO3
Test
20.00
20.00
16.67
16.67
Assignment
20.00
16.67
16.67
Final Exam
60.00
80.00
66.67
66.67
100
100
100
100
Total
15.
LO4
100.00
100
Details of Subject
Mode of Delivery
Topics
Lecture
4
Laboratory
4
2
2
2
4
10
8
8
4
26
22
1. Overview of Natural Language Processing
Definition, History of Natural Language Processing, Different Levels of
Language Analysis [Phonology, Morphology, Syntax, Semantics, and
Pragmatics], Applications [Text-based, and Dialogue-based, Natural
Language Front Ends to Databases or Knowledge-based Systems, Text
Generation, Machine Learning, Grammar Checker, and Speech
Recognition and Synthesis], Organisation of Natural Language
Understanding.
2. Linguistic Background
Basic English Syntax [Words, Phrase Structure such as Noun Phrases,
Verb Phrases, Adjective Phrases, Adverbial Phrases Morphology and the
Structure of Words, Grammar Structure].
3. Representation of Grammar
Tree Structure, Context Free Grammar (CFG) and, Transition Network
Grammar, Transforming the Grammar Structures into Prolog.
4. Syntactic Analysis
Parsing Technique [Top-down, Bottom-up, and Left-corner, Recursive
Transition Network (RTN) and Augmented Transition Network (RTN)
Parsers, Chart Parsers, Features and Unification, toward Efficient Parsing].
5. Semantics Analysis
Philosophical Issues in Semantics, Semantics and Logical Form for
English, Others Semantic Interpretation [Case Grammar, Semantic
Grammar, and Conceptual Dependency, Discourse and Anaphora
Problems].
Total
16.
Total Student Learning Time (SLT)
Face to Face
(Hour)
Total Guided and Independent Learning
Lecture
26
26
Laboratory
22
11
Presentation
1
3
Assignment
-
10
Mid Term Test
1
3
Final Exam
2
15
Sub Total
52
68
120
Total SLT
17.
Credit Value
18.
Reading Materials :
Textbook
1. Daniel Jurafsky, James H. Martin
(2009).
Speech
and
Language
Processing (2nd ed.). Prentice Hall.
3
Reference Materials
1.
Covington, M. A (1994). Natural Language
Processing For Prolog Programmers.
Prentice-Hall.