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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.