NLP Foundations

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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
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
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

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Natural Language Processing (NLP) is a highly dynamic research field
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
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.


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.


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).




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

Aanbevolen voorkennis

Programming (Python) and basics of Linguistics

Algemene informatie

Vakcode L_AAMPLIN023
Studiepunten 6 EC
Periode P4
Vakniveau 500
Onderwijstaal Engels
Faculteit Faculteit der Geesteswetenschappen
Vakcoördinator dr. R. Morante Vallejo
Examinator dr. R. Morante Vallejo
Docenten dr. R. Morante Vallejo

Praktische informatie

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