HIOK0002FU  Language Processing 2

Volume 2018/2019
Education

IT and Cognition

Content

Have you ever wondered how to build a system that can process text automatically, for example translate between languages, reveal syntactic structure, or extract semantic information?  

In this class we will explore fundamental algorithms and models for natural language processing, and describe the application of these algorithms and models to key problems in natural language processing, such as POS tagging, syntactic, semantic and discourse analysis, and machine translation. 

The main focus will be on the practical implementation of methods for syntactic parsing.

The format of the class consists of lectures (including guest lectures), student presentations, and project work.

 

Learning Outcome

The examinee is able to:

  • demonstrate theoretical insight into natural language processing by

identifying problems and solutions in the context of practical applications

  • apply feature extraction methods and modelling techniques in natural

language processing

  • deal with specific challenges arising from processing user-generated

content

  • evaluate systems or system components.

 

Academic objectives

See the curriculum:

MA-level 

2015 curriculum

 

Credit
7,5 ECTS
Type of assessment
Written examination
Written take-home assignment, set subject
Marking scale
7-point grading scale
Censorship form
No external censorship
  • Category
  • Hours
  • Class Instruction
  • 56
  • Course Preparation
  • 105
  • Exam Preparation
  • 45
  • Total
  • 206