Course Objective

Knowing the basic methods and technologies used in core NLP
technologies, knowing theoretical foundations and obtaining the skills
to find latest cutting-edge approaches as state-of-the-art software that
can be used in your own research. After finishing this course, students
will be able to come up with (basic) system designs and find the
necessary components for various NLP tasks.

Course Content

Natural Language Processing is a highly dynamic research field that
mainly operates on the interface between linguistics and computer
science. In order to get computers to deal well with natural language,
it is important to understand both how language works and how
computational methods work. Computational linguists work on this
interface and have developed methods and technologies for language
analysis. This course provides an
overview of these technologies for some of the core domains of Natural
Language Processing (morphology, syntax and (semantic) parsing,
semantics, discourse analysis). Students will be trained to find the
latest developments in this sometimes rapidly advancing field and,
specifically, where to find (more or less) ready-to-use tools that can
be used for various NLP tasks. The course is split up in two components:
one 6 ECTS component taught in period 4 and a 3 ECTS period taught in
period 5 which offers the
opportunity to either dive deeper into one of the core technologies
covered in the course or to investigate an application that makes use of
these technologies.

Teaching Methods

Interactive lectures and practical assignments

Method of Assessment

The evaluation of the first 6 ECTS part of the course consists of two
components. The course includes assignments about individual topics
covered.
Students will receive feedback and create a portfolio of these
assignments during the course (50%). There will also be an exam (50%).
At the end of the course, there is a final assignment for which they
write a report or paper which covers the supplementary 3ECTS of period
5.
Students will need to obtain a passing grade for both components to pass
the course.

Literature

Will be available on Canvas.

Target Audience

Students RM Human Language Technology.
Students from business analytics and computer science with some
background in NLP.

Recommended background knowledge

Python for Linguists, some experience in working in Commandline