Applied Text Mining


Course Objective

After you finished this course, you will be able to use existing
software for the analysis of natural language and to combine software to
automatically mine information from large amounts of text. You have the
knowledge to interpret the results and you can adjust the software

Course Content

In this course, you learn what it takes to build your own reading
machine. A reading machine is a complex piece of software that applies a
large variety of linguistic analyses to a text, each producing a
different type of output. You will learn to segment and tokenise text
and to detect the part-of-speech of words, how to split compounds,
detect entities, events and participants in text, detect emotions and
opinions, interprete temporal expressions, etc. Various aspects of
designing such systems are discussed: how to combine the output of
different modules, how to handle ambiguity, dependencies, error
propagation from one module to the next, how to design top-down,
bottom-up and hybrid approaches and how to involve background knowledge.
Finally, the result of the processing needs to be combined in output
data that the computer can understand and use for reasoning.

Teaching Methods

interactive lectures, assignments and practical classes.

Method of Assessment

The course is graded by the assignments and the results of the practical
classes (50%) and a final assignment (50%). Both components must be
graded at least 5.5.

Entry Requirements

Linguistic Research, Programming in Python, Deep Learning for NLP,
Language as Data


To be announced

Target Audience

Master students in (specialisation Text Mining) and master students with
a background in programming and NLP

General Information

Course Code L_PAMATLW002
Credits 6 EC
Period P3
Course Level 400
Language of Tuition English
Faculty Faculty of Humanities
Course Coordinator prof. dr. P.T.J.M. Vossen
Examiner prof. dr. P.T.J.M. Vossen
Teaching Staff prof. dr. P.T.J.M. Vossen

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: