Natural Language Processing and Information Retrieval Systems

This course presents the fundamental techniques of natural language processing and information retrieval systems. It aims to explain the potential and main limitations of these techniques. Some current research problems are introduced and some applications discussed and evaluated. Students will also be introduced to practical tasks in natural language processing.

Introduction to PLN; morphology of finite states; part-of-speech tagging; grammars; dependency structures; compositional semantics and lexical semantics; distributional semantics;; PLN research.

Information Retrieval; construction of indices; retrieval assessment: precision and recall, reference collections; queries: logical queries, queries ordered by relevance, vocabulary access structures, sequential vocabulary search, exact and approximate search.

Basic Information

60 hours
Data Structure and Algorithms


  • Croft, W. B., Metzler, D., & Strohman, T. (2009). Search Engines: Information Retrieval in Practice (1st ed.). Addison Wesley.
  • Ingwersen, P., & Järvelin, K. (2005). The Turn: Integration of Information Seeking and Retrieval in Context. Springer.
  • Manning, C. D., Raghavan, P., & Schütze, H. (2008). Introduction to Information Retrieval. Cambridge University Press.


  • Steven Bird, Ewan Klein and Edward Loper. Natural Language Processing with Python. O'Reilly. 2009
  • Nitin Indurkhya and Fred J. Damerau. Handbook of Natural Language Processing, Second Edition. Chapman & Hall / CRC. 2010
  • Baeza-Yates, R., & Ribeiro-Neto, B. (1999). Modern Information Retrieval. Addison Wesley.
  • Alexander Clark and Chris Fox. The Handbook of Computational Linguistics and Natural Language Processing. Wiley 2012
  • Matthew Honnibal and Patrick Harrison. Deep Learning with Text: A Modern Approach to Natural Language Processing with Python and Keras. O’Reilly, 2018
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