SLO | EN

Objectives and competences

The objective of this course is that students will be able to develop, test, and evaluate systems for natural language processing.

Content (Syllabus outline)

• Introduction: an overview of natural language processing and basic text processing. • Language modeling: probabilistic languages models, language model evaluation, and smoothing. • Spelling Correction: non-word spelling errors and real-word spelling errors. • Text classification: methods for text classification and evaluation of classification methods. • Sentiment analysis: basic methods for sentiment analysis and sentiment lexicons. • Semantics and WordNet: building semantic structures, semantic interpretation, meaning. • Statistical machine translation: basic methods for the statistical machine translation and evaluation of machine translation. • Information extraction and named entity recognition. • Advanced language technologies.

Learning and teaching methods

• Lectures: in lectures, students get to know the theoretical contents of the course. Lectures are conducted as classical lectures in frontal form, interleaved with discussions on practical examples of language technology. • Tutorials: in tutorial exercises, students are informed about lab work. • Lab work: in laboratory exercises, students work on individual tasks of language technologies with the help of scientific literature.

Intended learning outcomes - knowledge and understanding

• describe major trends in natural language processing, • develop, test, and evaluate systems for natural language processing, • create different corpora, • explain the importance of pragmatics in language technologies, • describe several standard methods for natural language processing

Intended learning outcomes - transferable/key skills and other attributes

• Communication skills: oral lab work defence, manner of expression at written examination. • Use of information technology: use of appropriate algorithms and software tools for natural language processing tasks.

Readings

• P. Jackson, I. Moulinier: Natural Language Processing for Online Applications: Text Retrieval, Extraction, and Categorization, Second Edition, John Benjamins, cop, Amsterdam, 2007. • Daniel Jurafsky and James H. Martin. Speech and Language Processing, 2nd edition. Pearson Prentice Hall, 2008. • Steven Bird, Ewan Klein in Edward Loper. Natural Language Processing with Python. O'Reilly Media, 2009. • Philipp Koehn, Statistical Machine Translation, Cambridge University Press, 2010. • Li Deng and Yang Liu. Deep Learning in Natural Language Processing, 1st edition, Springer, 2018.

Prerequisits

None.

  • doc. dr. BORKO BOŠKOVIĆ, univ. dipl. inž. rač. in inf.

  • Written examination: 50
  • Laboratory work: 50

  • : 30
  • : 30
  • : 120

  • Slovenian
  • Slovenian

  • COMPUTER SCIENCE AND INFORMATION TECHNOLOGIES - 1st