Kaisa_2012_3_photo by Veikko Somerpuro

Ilmoittaudu
11.2.2020 klo 09:00 - 9.3.2020 klo 23:59

Aikataulu

Tästä osiosta löydät kurssin opetusaikataulun. Tarkista mahdolliset muut aikataulut kuvauksesta.

PäivämääräAikaOpetuspaikka
Ma 9.3.2020
12:15 - 13:45
Ti 10.3.2020
10:15 - 11:45
Ma 16.3.2020
12:15 - 13:45
Ti 17.3.2020
10:15 - 11:45
Ma 23.3.2020
12:15 - 13:45
Ti 24.3.2020
10:15 - 11:45
Ma 30.3.2020
12:15 - 13:45
Ti 31.3.2020
10:15 - 11:45
Ma 6.4.2020
12:15 - 13:45
Ti 7.4.2020
10:15 - 11:45
Ma 20.4.2020
12:15 - 13:45
Ti 21.4.2020
10:15 - 11:45

Kuvaus

The course belongs to the MA Programme Linguistic Diversity in the Digital Age.

  • study track: language technology
  • modules: Studies in Language Technology (LDA-T3100), Essentials in Language Technology (LDA-TA500), Comprehensive specialization in Language Technology (LDA-TB500)

This is an optional course.

The course is available to students from other study tracks and degree programmes.

After successfully completing the course, students will be able to:

  • explain the common computational vector space models for words applied in language technology.
  • understand how words representations are used to generate sentence-based vector models.
  • describe the challenges related to word and sentence based vector models.
  • know the main neural language models and apply them for different applications.
  • read, understand and present scientific papers on current challenges in representation learning for natural language processing.

Students can take this course in year 1 or 2. The course is offered during the spring term in period 4.

  • Introduction to neural networks and semantic vector representations
  • Common model architectures for word-type models: CBOW, skip-gram, GloVe, fastText
  • Cross-lingual word embeddings
  • Common models for sentence embeddings and their application in NLP
  • Current approaches in language modeling
  • Recent topics in representation learning in natural language processing: multimodal learning for distributional representations, compositionality with distributed representations, multilingual representations, analyzing and interpreting neural networks for NLP, etc.
  • Weekly lectures and hands-on assignments
  • Final assignment with report
  • Seminar presentation on current topic
  • Dan Jurafsky and James H. Martin (2019, 3rd ed. draft). Speech and Language Processing.
  • Yoav Goldberg (2015). A Primer on Neural Network Models for Natural Language Processing.
  • Additional material distributed in the course.

Grading follows the standard scale 0 – 5. The following aspects are taken into account in grading:

  • Hands-on assignments (⅓)
  • Final assignment with report (⅓)
  • Seminar presentation on current topic (⅓)

Prerequisites:

  • Programming for linguists or equivalent (BA level)

Recommended optional studies:

  • Machine learning for linguists or equivalent (BA level)
  • Mathematics for linguists or equivalent (BA level)
  • Introduction to deep learning or equivalent (MA level)
  • Command-line course or equivalent