Learning outcomes

The student does not only have theoretic knowledge of the topics under consideration but also practical hands-on experience.

Content

This course studies language from the perspective of artificial intelligence, focusing on its role in communication. In essence, we will investigate how artificial agents are able to (learn to) understand the meaning underlying natural language utterances (comprehension), as well as express their ideas in the form of linguistic expressions (production). We examine how linguistic knowledge can be described in terms of form-meaning correspondences and how these correspondences can be formalized and implemented.

We will examine a range of well-known AI concepts and techniques, including knowledge representation, search-based problem solving, unification, meta-level architectures, and computational reflection.

Table of contents

  1. Introduction and Scientific Framework
  2. Modelling the Emergence of Language
  3. Computational Construction Grammar
  4. Representing Meaning
  5. Learning Language From Semantically Annotated Corpora
  6. Learning Language Through Intention Reading and Pattern Finding
  7. Narrative-based language understanding

 

Exercices

Hands-on exercises about the topics handled during the lectures.

Software to install: FCG Editor (https://www.fcg-net.org/download)

Assessment method

  • Oral exam with written preparation, closed book.
  • Theoretical questions and exercises.

Sources, references and any support material

Syllabus:

- Beuls, K., & Van Eecke, P. (2023). Natural Language Processing -- Course reader. [service de reprographie UNamur]

- Slides available through Webcampus

 

Language of instruction

Anglais