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List of Courses

Natural Language Processing

  • Course Code :
    CS443
  • Level :
    Undergraduate
  • Course Hours :
    3.00 Hours
  • Department :
    Department of Computer Science

Instructor information :

Area of Study :

Understand knowledge that enhances skills in fundamental area of computational properties of natural languages. Use and adopt fundamental and advanced computational linguistics. Evaluate effectively the merits of design the NLP system. Use all available principles and tools of natural language processing. Comprehend deeply the basic concepts of the computing linguistics. Develop and evaluate the architecture of NLP system process and components.

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Natural Language Processing

Fundamental concepts and ideas in natural language processing (NLP), also known as computational linguistics. It develops an in-depth understanding of both the algorithms available for the processing of linguistic information and the underlying computational properties of natural languages. Word level, syntactic, and semantic processing from both a linguistic and an algorithmic perspective are considered

For further information :

Natural Language Processing


Course outcomes:

a. Knowledge and Understanding:

1- Identify the fundamental concepts and theories related to NLP techniques
2- .Explain the principles and techniques to identify the criteria for current use and future development of different machine learning techniques used in NLP
3- Describe different qualitative and quantitative used in syntax, semantics, and other components of natural language processing

b. Intellectual Skills:

1- Analyze different natural language problems and setting goals and requirements
2- Propose a set of alternative solutions for a given NLP problem associated with their results
3- Select appropriate methodologies and techniques for a given NLP problems solutions and setting out their limitations, restrictions and errors

c. Professional and Practical Skills:

1- Analyze, design, implement and test NLP systems
2- Apply, design methodologies, programming languages, database systems and different supporting tools for NLP systems

d. General and Transferable Skills:

1- Work in a team to develop the requirement documentation
2- Apply communication skills in presentations and report writing using various methods and tools


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Natural Language Processing

Course topics and contents:

Topic No. of hours Lecture Tutorial/Practical
Introduction To NLP 4 2 2
Finite State Automata 4 2 2
Regular Expressions 4 2 2
Words and Transducers 4 2 2
Formal Grammar of English and Syntactic Parsing 4 2 2
Semantics and Pragmatics 4 2 2
Computational Semantics 4 2 2
Computational Discourse 4 2 2
Mid Term Exam 2
Knowledge Discovery, Text Mining 4 2 2
Summarization 4 2 2
Query Answering 4 2 2
Revision 4 2 2
Final Exam 2

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Natural Language Processing

Teaching And Learning Methodologies:

Teaching and learning methods
Interactive Lectures including Discussions
Practical Lab Sessions
Self-Study (Project / Reading Materials / Online Material / Presentations)
Case Studies
Brain Storming and Problem Solving

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Natural Language Processing

Course Assessment :

Methods of assessment Relative weight % Week No. Assess What
Assignments 5.00 4
Practical Exam 10.00 10
Presentations 5.00 11
Quizzes 10.00 5
Team Work Projects 10.00 13
Written Final Exam 40.00 14
Written Midterm Exam (s) 20.00 9

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Natural Language Processing

Books:

Book Author Publisher

Course notes :

Course Notes are available with all the slides used in lectures in electronic form on Learning Management System (Moodle)

Recommended books :

Artificial Intelligent : A Modern Approach - (Third edition) by Stuart Russell and Peter Norvig, Publisher: Prentice Hall, latest edition.

Web Sites :

www.ekb.eg www.ai.com

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