Natural Language Processing 2

6 EC

Semester 2, period 5

52042NLP6Y

Owner Master Artificial Intelligence
Coordinator prof. dr. Khalil Sima'an
Part of Master Artificial Intelligence, Master Logic,

Course manual 2021/2022

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, particularly in machine translation
  • 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

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%

Paper Presentation

0.28%

Question submission for paper 1

0.28%

Question submission for paper 2

0.285%

Question Submission for paper 3

0.28%

Question submission for paper 4

0.28%

Question Submission for paper 10

0.28%

Question Submission for paper 11

0.28%

Question Submission for paper 12

0.28%

Question Submission for paper 13

0.28%

Question Submission for paper 14

0.28%

Question Submission for paper 15

0.28%

Question Submission for paper 16

0.28%

Question submission for paper 17

0.28%

Question submission for paper 18

0.28%

Question submission for paper 5

0.28%

Question submission for paper 6

0.28%

Question Submission for paper 7

0.28%

Question Submission for paper 8

0.28%

Question Submission for paper 9

35%

Research Project 1

35%

Research Project 2

5%

Lab 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

Timetable

The schedule for this course is published on DataNose.

Additional information

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

Contact information

Coordinator

  • prof. dr. Khalil Sima'an

Staff