NLP Foundations

2019-2020

Course Objective

The ability to:
1. find out what a specific NLP task consists of (what the goal of the
task is, what the evaluation data looks like) and explain this to others
2. identify what (linguistic) information is relevant for addressing a
specific task and what analyses are involved in obtaining this
information
3. find out what the state-of-the-art methods and open issues for a
specific task are by reading and reflecting on appropriate research
papers
4. design systems for various NLP tasks for specific languages (and
implement them given availability of appropriate resources) and
5. find and apply available resources (annotated data,
dictionaries/ontologies, tools)
6. carry out appropriate evaluations of NLP technologies and reflect on
the outcome of these evaluations.
7. describe their questions, insights, findings and proposals in
academic writing style

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 the basics of technologies and computational models
for core domains of natural language processing (morphology, syntax and
(semantic) parsing, semantics) as well as their role in various NLP
applications. The main focus of the course is to learn how to analyze
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

The course consist of two lectures per week (each two hours): one
focuses mainly on foundations and the other more on practical aspects of
NLP. All course goals are practiced in exercises during the course.

Method of Assessment

Students submit revised versions of their assignments by the end of the
course as a portfolio. This will mainly test practical and reflection
skills. There is an exam for testing knowledge of the foundations
(possibly a take-home exam). The portfolio and exam each make up 50% of
the grade. Students need to obtain a passing grade for both components
(5.5 or higher).

Literature

TBA

Target Audience

Students of the Text Mining Masters. This course can also be followed by
students of other programs with appropriate background knowledge. We
recommend that students from computer science follow the course offered
as part of AI instead. Note that it is not possible to obtain ECTS
credits for both this course and the course offered as part of the AI
Masters.

Recommended background knowledge

Programming (Python) and basics of Linguistics

General Information

Course Code L_AAMPLIN023
Credits 6 EC
Period P4
Course Level 500
Language of Tuition English
Faculty Faculty of Humanities
Course Coordinator dr. A.S. Fokkens
Examiner dr. A.S. Fokkens
Teaching Staff

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: