University of West Attica

Artificial Intelligence and Data Science

Natural language processing, semantic web & social networks analysis

Course info:

Semester: 6

General Foundation

ECTS: 6

Hours per week: 2

Professor: T.B.D.

Teaching style: Face to face, distance learning

Grading: 2 individual assignments and a team project

Activity Workload
Lectures 26
Team work 58
Independent study 66
Course total 150

Learning Results

The postgraduate students who complete successfully this course will gain a foundational understanding in Natural Language Processing methods and techniques. They will also:

  • Gain knowledge of the principles of Natural Language Processing, Computational Linguistics, the Semantic Web and Social Network Analysis
  • Get specialized skills in problem-solving that arise during the development of Information Extraction Systems required in research and innovation in order to contribute to the development of new knowledge and processes.
  • Be critical of cutting-edge knowledge issues in innovative fields such as Opinion Mining
  • Have the ability to manage complex Semantic Web environments and apply new strategic approaches to address unforeseen problems that arise during their management.
  • Understand the fundamental concepts in analysing the large-scale data that are derived from social networks

Skills acquired

  • Retrieve, analyse and synthesise data and information, with the use of necessary technologies
  • Work individually and in teams
  • Advance free, creative and causative thinking
  • Introduction in Computational Linguistics
  • Natural Language Processing and Applications
  • Information Extraction
  • Natural Language Processing and the Semantic Web
  • Automatic Ontology development
  • Natural Language Processing and Social Networks
  • Sentiment Analysis and Opinion Mining
  • Introduction to Social Network Mining, Graph Models and Node Metrics
  • Social-Network Graph Analysis
  • Information Diffusion in Social Networks
  1. Bird Steven, Klein Ewan & Loper Edward (2009) Natural Language Processing with Python: Analyzing Text with the Natural Language Toolkit, O’Reilly Media, 2009, http://www.nltk.org/book/
  2. Clark Alexander, Fox Chris, Lappin Shalom (eds) (2010) The Handbook of Computational Linguistics and Natural Language Processing, Wiley-Blackwell
  3. Heath, Tom and Christian Bizer, Christian (2011) Linked Data: Evolving the Web into a Global Data Space (1st edition). Synthesis Lectures on the Semantic Web: Theory and Technology, 1:1, 1-136. Morgan & Claypool.
  4. Jurafsky, Daniel & Martin, James. (2008). Speech and Language Processing: An Introduction to Natural Language Processing, Computational Linguistics, and Speech Recognition (2nd edition), Prentice Hall.
  5. Liu, Bing (2012). Sentiment Analysis and Opinion Mining, Synthesis Lectures on Human Language Technologies, Vol. 5, No. 1 , Pages 1-167, Morgan & Claypool
  6. Maynard, Diane, Bontcheva Kalina, Augenstein Isabelle (2016). Natural Language Processing for the Semantic Web, Synthesis Lectures on the Semantic Web: Theory and Technology, December 2016, Vol. 6, No. 2 , Pages 1-194, Morgan & Claypool, (https://doi.org/10.2200/S00741ED1V01Y201611WBE015)
  7. Wilks, Yorick and Brewster, Christopher (2009), Natural Language Processing as a Foundation of the Semantic Web, Foundations and Trends in Web Science, Vol. 1, Nos. 3–4, 199–327, Morgan & Claypool, http://dx.doi.org/10.1561/1800000002 Professional, 2011
  8. David Easley and Jon Kleinberg, Networks, crowds, and markets, Cambridge University Press, 2010.
  9. Jure Leskovec, Anand Rajaraman and Jeffrey David Ullman, Mining of massive datasets, Cambridge University Press, 2014
  10. John Scott 2000 Network Analysis: A Handbook. Second Edition. Newbury Park CA: Sage.
  11. Tom Valente 2010 Social Networks and Health: Models, Methods and Applications, First Edition. Oxford University Press
  12. Charles Kadushin 2011 Understanding Social Networks: Theories, Concepts and Findings, First Edition. Oxford University Press.
  13. Stanley Wasserman and Katherine Faust 1994 Social Network Analysis: Methods and Applications. First Edition. Cambridge University Press.

Websites:

Related scientific journals:

Learning Results - Skills acquired

Learning Results

The postgraduate students who complete successfully this course will gain a foundational understanding in Natural Language Processing methods and techniques. They will also:

  • Gain knowledge of the principles of Natural Language Processing, Computational Linguistics, the Semantic Web and Social Network Analysis
  • Get specialized skills in problem-solving that arise during the development of Information Extraction Systems required in research and innovation in order to contribute to the development of new knowledge and processes.
  • Be critical of cutting-edge knowledge issues in innovative fields such as Opinion Mining
  • Have the ability to manage complex Semantic Web environments and apply new strategic approaches to address unforeseen problems that arise during their management.
  • Understand the fundamental concepts in analysing the large-scale data that are derived from social networks

Skills acquired

  • Retrieve, analyse and synthesise data and information, with the use of necessary technologies
  • Work individually and in teams
  • Advance free, creative and causative thinking
Course content
  • Introduction in Computational Linguistics
  • Natural Language Processing and Applications
  • Information Extraction
  • Natural Language Processing and the Semantic Web
  • Automatic Ontology development
  • Natural Language Processing and Social Networks
  • Sentiment Analysis and Opinion Mining
  • Introduction to Social Network Mining, Graph Models and Node Metrics
  • Social-Network Graph Analysis
  • Information Diffusion in Social Networks
Recommended bibliography
  1. Bird Steven, Klein Ewan & Loper Edward (2009) Natural Language Processing with Python: Analyzing Text with the Natural Language Toolkit, O’Reilly Media, 2009, http://www.nltk.org/book/
  2. Clark Alexander, Fox Chris, Lappin Shalom (eds) (2010) The Handbook of Computational Linguistics and Natural Language Processing, Wiley-Blackwell
  3. Heath, Tom and Christian Bizer, Christian (2011) Linked Data: Evolving the Web into a Global Data Space (1st edition). Synthesis Lectures on the Semantic Web: Theory and Technology, 1:1, 1-136. Morgan & Claypool.
  4. Jurafsky, Daniel & Martin, James. (2008). Speech and Language Processing: An Introduction to Natural Language Processing, Computational Linguistics, and Speech Recognition (2nd edition), Prentice Hall.
  5. Liu, Bing (2012). Sentiment Analysis and Opinion Mining, Synthesis Lectures on Human Language Technologies, Vol. 5, No. 1 , Pages 1-167, Morgan & Claypool
  6. Maynard, Diane, Bontcheva Kalina, Augenstein Isabelle (2016). Natural Language Processing for the Semantic Web, Synthesis Lectures on the Semantic Web: Theory and Technology, December 2016, Vol. 6, No. 2 , Pages 1-194, Morgan & Claypool, (https://doi.org/10.2200/S00741ED1V01Y201611WBE015)
  7. Wilks, Yorick and Brewster, Christopher (2009), Natural Language Processing as a Foundation of the Semantic Web, Foundations and Trends in Web Science, Vol. 1, Nos. 3–4, 199–327, Morgan & Claypool, http://dx.doi.org/10.1561/1800000002 Professional, 2011
  8. David Easley and Jon Kleinberg, Networks, crowds, and markets, Cambridge University Press, 2010.
  9. Jure Leskovec, Anand Rajaraman and Jeffrey David Ullman, Mining of massive datasets, Cambridge University Press, 2014
  10. John Scott 2000 Network Analysis: A Handbook. Second Edition. Newbury Park CA: Sage.
  11. Tom Valente 2010 Social Networks and Health: Models, Methods and Applications, First Edition. Oxford University Press
  12. Charles Kadushin 2011 Understanding Social Networks: Theories, Concepts and Findings, First Edition. Oxford University Press.
  13. Stanley Wasserman and Katherine Faust 1994 Social Network Analysis: Methods and Applications. First Edition. Cambridge University Press.

Websites:

Related scientific journals: