Universität Wien FIND

Bedingt durch die COVID-19-Pandemie können kurzfristige Änderungen bei Lehrveranstaltungen und Prüfungen (z.B. Absage von Vor-Ort-Lehre und Umstellung auf Online-Prüfungen) erforderlich sein. Melden Sie sich für Lehrveranstaltungen/Prüfungen über u:space an, informieren Sie sich über den aktuellen Stand auf u:find und auf der Lernplattform moodle.

Weitere Informationen zum Lehrbetrieb vor Ort finden Sie unter https://studieren.univie.ac.at/info.

052315 VU Natural Language Processing (2021S)

Prüfungsimmanente Lehrveranstaltung
Mo 08.03. 11:30-14:45 Digital

An/Abmeldung

Details

max. 25 Teilnehmer*innen
Sprache: Englisch

Lehrende

Termine (iCal) - nächster Termin ist mit N markiert

This course is exclusively online.

Montag 01.03. 11:30 - 14:45 Digital
Montag 15.03. 11:30 - 14:45 Digital
Montag 22.03. 11:30 - 14:45 Digital
Montag 12.04. 11:30 - 14:45 Digital
Montag 19.04. 11:30 - 14:45 Digital
Montag 26.04. 11:30 - 14:45 Digital
Montag 03.05. 11:30 - 14:45 Digital
Montag 10.05. 11:30 - 14:45 Digital
Montag 17.05. 11:30 - 14:45 Digital
Montag 31.05. 11:30 - 14:45 Digital
Montag 07.06. 11:30 - 14:45 Digital
Montag 14.06. 11:30 - 14:45 Digital
Montag 21.06. 11:30 - 14:45 Digital
Montag 28.06. 11:30 - 14:45 Digital

Information

Ziele, Inhalte und Methode der Lehrveranstaltung

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.

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 corresponding chapters from the NLTK book are made available as Jupyter Notebooks through Moodle.

The main software tools used in this course are Python 3, NLTK, and Jupyter Notebooks for submission and presentation of exercises.

Art der Leistungskontrolle und erlaubte Hilfsmittel

There is one oral exam at the end of the course. The result accounts 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 best result for the first 4 exercise sheets accounts for 30 % of the total rating, the best result for the remaining 6 exercise sheets accounts for the final 30 %.

Mindestanforderungen und Beurteilungsmaßstab

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

This course is exclusively online, all the oral presentations and exams are carried out via video calls.

The exercise sheets are provided as Jupyter Notebooks through Moodle assignments and must be submitted until the indicated deadlines. After each deadline, one solution for each exercise will be chosen randomly,
giving priority to students with a lower percentage (0 % - 30 %) achieved so far for this rating aspect. The selected students will be contacted to choose a time slot for a remote oral presentation.

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

Prüfungsstoff

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 exam covers theoretical aspects from the corresponding chapters of the NLTK book, no coding is required for the exam.

Literatur

- Steven Bird, Ewan Klein, and Edward Loper. Natural Language Processing with Python. http://www.nltk.org/book/, O'Reilly Media, Inc., 2009.
- Kai-Uwe Carstensen et al., eds. Computerlinguistik und Sprachtechnologie - Eine Einführung. 3rd Edition, Springer Spektrum, 2010 (in German).
- Benjamin Bengfort, Rebecca Bilbro, Tony Ojeda. Applied Text Analysis with Python. O'Reilly Media, Inc., 2018.
- Hobson Lane, Cole Howard, Hannes Hapke. Natural Language Processing in Action. Understanding, analyzing, and generating text with Python. Manning Publications, 2019.
- Jacob Eisenstein. Introduction to Natural Language Processing. MIT Press, 2019.
- Delip Rao, Brian McMahan. Natural Language Processing with PyTorch. O'Reilly Media, Inc., 2019 (also available in German).
- Sowmya Vajjala, Bodhisattwa Majumder, Anuj Gupta, Harshit Surana. Practical Natural Language Processing. O'Reilly Media, Inc., 2020.

Zuordnung im Vorlesungsverzeichnis

Module: NLP MSP

Letzte Änderung: Mi 17.02.2021 14:28