Natural Language Processing Technology

2019-2020

Course Objective

1. Know what the most common NLP tasks consist of (what the goal of the
tasks are, what the evaluation data looks like, what their main
approaches are) and explain this to others. (Knowledge and
Understanding)
2. Being able to identify what (linguistic) information is relevant for
addressing a specific task and what analyses are involved in obtaining
this information. (Applying Knowledge and Understanding)
3. Be familiar with the most commonly used machine learning approaches.
(Knowledge and Understanding)
4. Being able to out what the state-of-the-art methods and open issues
for a specific task are by reading and reflecting on appropriate
research papers. (Making Judgments)
5. Being able to design systems for various NLP tasks for specific
languages (and implement them given availability of appropriate
resources) & to find and apply available resources (annotated data,
dictionaries/ontologies, tools, programming packages). (Applying
Knowledge and Understanding)
6. Being able to carry out appropriate evaluations of NLP technologies
and reflect on the outcome of these evaluations. (Applying Knowledge and
Understanding, Making Judgments)
7. Being able to describe their questions, insights, findings and
proposals in academic writing style. (Communication)

Course Content

Natural Language Processing (NLP) 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 covers technologies and computational models for core
domains of natural language processing (morphology, syntax and
(semantic) parsing, semantics) as well as at least one more applied NLP
task. The main focus of the course is to learn how to analyse various
NLP tasks: what information is relevant? What linguistic properties need
to be dealt with? How can this information be identified and used while
creating computational models and tools? Students are trained to find,
understand and work with the latest developments in this sometimes
rapidly advancing field. The course includes practical components where
we learn to find (more or less) ready-to-use tools that can be used for
various NLP tasks, apply them and reflect on the consequences of
underlying technologies on the performance of the tools.

Teaching Methods

There are two classes a week: one with a more technical/practical focus
and one with a more linguistic/theoretical focus. Students actively
carry out exercises during the course and get the chance to improve them
based on feedback for their final portfolio.

Method of Assessment

Students will create a portfolio of exercises covering practical
components as well as the more reflective parts of the course. Overall
insights and knowledge are tested through an exam (which may be a
take-home exam). The average of these two components form the grade.
Both components need to have a passing grade (5.5 or higher) in order to
pass the course.

Literature

TBA

Target Audience

Masters AI, also suitable for Business Analytics and other Computer
Science students

Recommended background knowledge

Programming skills (preferably Python)

General Information

Course Code L_AAMAALG005
Credits 6 EC
Period P5
Course Level 400
Language of Tuition English
Faculty Faculty of Science
Course Coordinator dr. A.S. Fokkens
Examiner dr. A.S. Fokkens
Teaching Staff dr. A.S. Fokkens

Practical Information

You need to register for this course yourself

Last-minute registration is available for this course.

Teaching Methods Seminar
Target audiences

This course is also available as: