Natural Language Processing 2

6 EC

Semester 2, period 5

52042NLP6Y

Owner Master Artificial Intelligence
Coordinator dr. A. Lucic
Part of Master Artificial Intelligence, Master Logic, Master Logic, track Logic and Computation, Master Logic, track Logic and Philosophy, Master Logic, track Logic and Language, Master Logic, track Logic and Mathematics, Master Logic, track Logic Year, year 1Master Logic, track Exchange, year 1

Course manual 2024/2025

Course content

The amount of language data that is available to us electronically is increasing with the day. With this eminent increase, a question arises as to the possibility of inducing latent structure in this data that can be useful for further tasks such as machine translation. The different kinds of latent structure that is possible depends on the data and the task, and will usually demand suitable statistical models and learners. The course will teach methods for inducing a variety of latent structure for tasks such as language modeling, machine translation and adaptation across domains. The course covers the following topics

  • Statistical Machine Translation and Paraphrasing
  • Domain adaptation
  • Statistical models of lexical semantics
  • Latent, hierarchical or linguistic structure in natural language data 


Study materials

Literature

Other

  • Articles will be provided.

Objectives

  • Acquire knowledge of advanced NLP techniques
  • Acquire awareness of ongoing research and challenges in NLP in general
  • Practice reading and presenting scientific articles in NLP
  • Develop, program and report basic solutions to example problems within multiple small projects
  • Practice writing an article to report the results of research on a mini-project

Teaching methods

  • Lecture
  • Seminar
  • Working independently on e.g. a project or thesis
  • Presentation/symposium

Lectures by lecturers; Preparation and presentation of articles by students; practical training and project work

Learning activities

Activity

Hours

Hoorcollege

24

Laptopcollege

14

Tentamen

3

Self study

127

Total

168

(6 EC x 28 uur)

Attendance

This programme does not have requirements concerning attendance (OER part B).

Assessment

Item and weight Details

Final grade

20%

Midterm report

Must be ≥ 5.5, NAP if missing

15%

Paper presentation

Must be ≥ 5.5, NAP if missing

10%

Paper Moderation

Must be ≥ 5.5, NAP if missing

50%

Research Project

Must be ≥ 5.5, NAP if missing

5%

Lecture attendance

Fraud and plagiarism

The 'Regulations governing fraud and plagiarism for UvA students' applies to this course. This will be monitored carefully. Upon suspicion of fraud or plagiarism the Examinations Board of the programme will be informed. For the 'Regulations governing fraud and plagiarism for UvA students' see: www.student.uva.nl

Course structure

Weeknummer Onderwerpen Studiestof
1
2
3
4
5
6
7
8

Additional information

Recommended prior knowledge: Natural Language Processing 1; Machine Learning: Pattern Recognition.

Contact information

Coordinator

  • dr. A. Lucic

Staff

  • Seth Aycock MSc
  • A. Bavaresco MSc
  • Evgenia Ilia MSc
  • J. Lin MSc
  • prof. dr. Khalil Sima'an