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Course List

Natural Language Processing

  • Course Code :
    CSC 443
  • Level :
    Undergraduate
  • Course Hours :
    3.00 Hours
  • Department :
    Department of Computer Science

Instructor information :

Area of Study :

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

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Natural Language Processing

Introduction to computational linguistics in general and natural language processing in particular. Reviewing background material in linguistics and surveying works on topics such as sub-languages, syntactic analysis, context, discourse analysis, application or world knowledge, machine translation, and text generation. Arabic computational linguistics

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Natural Language Processing

Course outcomes:

a. Knowledge and Understanding:

1- Identify and discuss the characteristics of different NLP techniques
2- Identify and discuss the characteristics of different machine learning techniques used in NLP
3- Understand basic processes and representations used in syntax, semantics, and other components of natural language processing
4- Understand the design of tools for basic NLP tasks such as tagging and partial parsing and be able to apply them to text
5- Understand some of the basic principles of the representation of linguistic meaning and interpretative inference

b. Intellectual Skills:

1- Learn about the areas in which natural language processing is being applied to problems today.
2- Be able to describe and discuss the potential and limitations of NLP techniques for applications such as machine translation, question answering, information retrieval and information extraction

c. Professional and Practical Skills:

1- Be able to implement programs for NLP algorithms and techniques

d. General and Transferable Skills:

1- discuss the characteristics of different NLP techniques
2- discuss the characteristics of different machine learning techniques used in NLP
3- Be able to describe and discuss the potential and limitations of NLP techniques for applications such as machine translation, question answering, information retrieval and information extraction
4- Be able to describe and discuss some of the proposed ideas in any included topic
5- Be able to provide his own perspective considering selections of the published ideas

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Natural Language Processing

Course topics and contents:

Topic No. of hours Lecture Tutorial/Practical
Introduction To NLP 3 2 2
Knowledge Discovery 3 2 2
Text Mining 3 2 2
Summarization 3 2 2
Query Answering 3 2 2
Finite State Automata 3 2 2
Regular Expressions 3 2 2
Words and Transducers 3 2 2
Formal Grammar of English and Syntactic Parsing 3 2 2
Semantics and Pragmatics 3 2 2
Computational Semantics 3 2 2
Computational Discourse 3 2 2
Applications (Information Retrieval) 3 2 2

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Natural Language Processing

Teaching And Learning Methodologies:

Teaching and learning methods
Practical training
Lectures
Exercises
Open Discussion
Self Studies
Web-Site searches
Case Study
Projects
Presentation

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Natural Language Processing

Course Assessment :

Methods of assessment Relative weight % Week No. Assess What

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Natural Language Processing

Books:

Book Author Publisher
Natural Language Processing: Semantic Aspects Epaminondas Kapetanios / Doina Tatar CRC
Speech and Language Processing Daniel Jurafsky Prentice Hall

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