Course ObjectiveThis course provides students with the means to conduct NLP research
using machine learning. Students will learn: a) what the main machine
learning technologies used in Natural Language Processing are b) how
they work and how they can be used c) the methodologies for using these
technologies in NLP research and d) how to represent linguistic data and
what the impact is of choices in data representation. By the end of this
course, students will be able to (1) name and describe the (working of)
main machine learning technologies in NLP, (2) apply these technologies
to specific NLP tasks (3) design a research environment where machine
learning is used to solve an NLP problem, and (4) interpret and analyze
evaluation results from machine learning experiments.
Course ContentMachine learning is a dynamic and active research field. The main goal
of machine learning is to develop systems which can automatically solve
different problems without being specifically programmed, i.e. by
learning from the data. In this course, we will focus on the use of
machine learning as a methodology for solving NLP tasks (e.g.
pos-tagging, syntactic parsing, information extraction). We cover both
`traditional' machine learning methods as the latest deep learning
approaches. Representation of language as data plays a prominent role in
Particular attention will be paid to the methodologies for using machine
learning in NLP research. We will cover the experimental setup, running
existing packages on new tasks and evaluation of overall results as well
as error analysis. The course covers practical skills that can be
useful in industry as well as in academia.
The course can be followed by any student with sufficient linguistic and
Teaching Methods2 lectures of 2 hours. One focuses on machine learning algorithms, the
other on linguistic properties and practical aspects.
Method of AssessmentStudents hand in a portfolio of exercises carried out during the course
(50%) and take a final exam (50%). Both components need to receive a
passing grade in order to pass the course (at least 5.5).
Entry RequirementsProgramming (Python) and Linguistic foundations
Target AudienceThis course is specifically designed for students in the Text Mining
1-year master. It can also be followed by Computer Science students
(among others Business Analytics and AI).
|Language of Tuition||English|
|Faculty||Faculty of Humanities|
|Course Coordinator||dr. A.S. Fokkens|
|Examiner||dr. A.S. Fokkens|
You need to register for this course yourself
Last-minute registration is available for this course.
This course is also available as: