Skip to content
master
Switch branches/tags
Code

Latest commit

 

Git stats

Files

Permalink
Failed to load latest commit information.
Type
Name
Latest commit message
Commit time
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 

(LT2202 V17) FACULTY OF ARTS LT2202, Statistical methods, 7,5 higher education credits Statistiska metoder, 7.5 högskolepoäng (Second Cycle)

This repository contains course materials developed for the course on Statistical Methods in Natural Language Processing, at the University of Gothenburg.

RandomVariable

Confirmation

The course syllabus was confirmed by The Faculty of Arts on 2011-01-17 to be valid from 2011-01-19. Field of education: Science 100 % Department: Department of Philosophy, Linguistics and Theory of Science

Position in the educational system

The course is part of the 'Master in Language Technology' programme (H2MLT). It can also be offered as a freestanding course.

Main field of studies

Language Technology

Specialization

A1F, Second cycle, has second-cycle course/s as entry requirements

Entry requirements

Passed courses:

  • LT2103, Natural Language Processing
  • LT2104, Programming for NLP

or

  • equivalent language technological skills and knowledge

Course content

The purpose of this course is to give an introduction to probabilistic modeling, statistical methods and their use within the field of language technology. The following topics will be covered in the course:

  • Probability theory
  • Information theory
  • Statistical theory (sampling, estimation, hypothesis testing)
  • Language modeling •Part-of-speech tagging
  • Syntactic parsing
  • Word sense disambiguation
  • Machine translation
  • Evaluation
  • Learning outcomes

After completion of the course the student is expected to be able to:

Knowledge and understanding

  • account for basic notions of probability theory, information theory and statistical theory •give examples of how statistical methods have been applied in language technology systems Skills and abilities
  • apply statistical techniques to the development of language technology systems •evaluate language technology applications using standard statistical tests Judgement and approach
  • choose the appropriate statistical method for a particular task
  • evaluate the significance of statistical results

Literature

Literature will be permanent eight weeks before course start.

Assessment

There are laboratory exercises that require attendance for a passing grade. The examination consists of: participation in laboratory exercises, assignments, and possibly a written exam.

Grading scale

The grading scale comprises Fail (U), Pass (G), Pass with Distinction (VG).

Requirements for Pass:

  • completed assignments *participation in laboratory exercises
  • passed written exam (if any) Requirements for Pass with distinction
  • completed assignments of good quality
  • participation in laboratory exercises
  • written exam (if any) passed with distinction

A student who has failed an examination twice has the right to change examiners if it is feasible. A written application should be sent to the Board of the Department of Philosophy, Linguistics and Theory of Science.

About

Statistical Methods for Natural Language Processing (NLP). Master’s Programme in Language Technology; teaching and course organizing.

Resources

Releases

No releases published

Packages

No packages published