Universität Wien FIND
Please note: From 11 March 2020 until 4 April 2020, no courses/exams with student attendance will take place at the University of Vienna. This applies to courses and exams in all degree programmes. Teaching will instead take place in the form of learning at home and e-learning. For further information, please visit our website at https://www.univie.ac.at/en/about-us/further-information/coronavirus/.

052315 VU Natural Language Processing (2020S)

Continuous assessment of course work

Registration/Deregistration

Details

max. 25 participants
Language: English

Lecturers

Classes (iCal) - next class is marked with N

Monday 02.03. 13:15 - 14:45 PC-Unterrichtsraum 2, Währinger Straße 29 1.OG
Monday 09.03. 11:30 - 14:45 PC-Unterrichtsraum 2, Währinger Straße 29 1.OG
Monday 16.03. 11:30 - 14:45 PC-Unterrichtsraum 2, Währinger Straße 29 1.OG
Monday 23.03. 11:30 - 14:45 PC-Unterrichtsraum 2, Währinger Straße 29 1.OG
Monday 20.04. 11:30 - 14:45 PC-Unterrichtsraum 2, Währinger Straße 29 1.OG
Monday 27.04. 11:30 - 14:45 PC-Unterrichtsraum 2, Währinger Straße 29 1.OG
Monday 04.05. 11:30 - 14:45 PC-Unterrichtsraum 2, Währinger Straße 29 1.OG
Monday 11.05. 11:30 - 14:45 PC-Unterrichtsraum 2, Währinger Straße 29 1.OG
Monday 18.05. 11:30 - 14:45 PC-Unterrichtsraum 2, Währinger Straße 29 1.OG
Monday 25.05. 11:30 - 14:45 PC-Unterrichtsraum 2, Währinger Straße 29 1.OG
Monday 08.06. 11:30 - 14:45 PC-Unterrichtsraum 2, Währinger Straße 29 1.OG
Monday 15.06. 11:30 - 14:45 PC-Unterrichtsraum 2, Währinger Straße 29 1.OG
Monday 22.06. 11:30 - 14:45 PC-Unterrichtsraum 2, Währinger Straße 29 1.OG
Monday 29.06. 11:30 - 14:45 PC-Unterrichtsraum 2, Währinger Straße 29 1.OG

Information

Aims, contents and method of the course

The students know the basics of natural language processing. They master the use of linguistic resources and tools, and are able to efficiently apply them to independently design and implement solutions for subject-specific problems. Students can convey this knowledge in written form and in oral presentations.

This is a practice-oriented course with a significant implementation requirement. It is based on the NLTK book with many implementation examples in Python. Selected problems are also solved using SWI-Prolog.

This course covers the following topics: language processing and Python, accessing text corpora and lexical resources, processing raw text, writing structured programs, categorizing and tagging words, learning to classify text, extracting information from text, analyzing sentence structure, building feature based grammars, analyzing the meaning of sentences.

The main software tools used in this course are: Python 3 and NLTK with bpython as interpreter and Geany as editor; as well as SWI-Prolog with the PDT Eclipse Prolog IDE.

COVID-19 Update: Due to the current situation, this information may be subject to change. The participants will be informed immediately of any necessary adaptations.

Assessment and permitted materials

There are two exams, one after the first half of the semester and one at the end of the semester. For each exam there are 80 minutes to answer 20 questions. Each correct answer counts 1 point. No support material is allowed.

All electronic devices must be turned off and put away before starting the exam. They must not be kept on the person or placed in clothes but packed in, e.g. a closed bag and cannot be taken out during the entire exam.

The two test results account for 40 % of the total rating.

The remaining 60 % are earned through voluntary oral presentations during the semester. There are altogether 10 exercise sheets with problems to solve. For a certain exercise sheet at most one problem can be presented by a student. The two best results for the first 5 exercise sheets account for 30 % of the total rating, the two best results for the second 5 exercise sheets account for the final 30 %.

COVID-19 Update: Due to the current situation, this information may be subject to change. The participants will be informed immediately of any necessary adaptations.

Minimum requirements and assessment criteria

A mandatory prerequisite for this course is the successful completion of Foundations of Data Analysis.

The grading scale for the course is: 1: at least 90%, 2: at least 80%, 3: at least 65%, 4: at least 50%.

Examination topics

There are exercise sheets for the following topics: language processing and Python, accessing text corpora and lexical resources, processing raw text, writing structured programs, categorizing and tagging words, learning to classify text, extracting information from text, analyzing sentence structure, building feature based grammars, analyzing the meaning of sentences. The first exam covers the first five topics, the second exam the remaining topics.

Reading list

Steven Bird, Ewan Klein, and Edward Loper. Natural Language Processing with Python. http://www.nltk.org/book/, O'Reilly Media, 2009.

Daniel Jurafsky and James H. Martin. Speech and Language Processing. 2nd Edition, Pearson, 2009.

Ruslan Mitkov, ed. The Oxford Handbook of Computational Linguistics. Oxford University Press, 2005.

Nitin Indurkhya and Fred J. Damerau, eds. Handbook of Natural Language Processing. 2nd Edition, Chapman and Hall/CRC, 2010.

Kai-Uwe Carstensen et al., eds. Computerlinguistik und Sprachtechnologie - Eine Einführung. 3rd Edition, Springer Spektrum, 2010 (in German).

Association in the course directory

Module: NLP MSP

Last modified: Sa 28.03.2020 12:07