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LT2203 VT19 Computational Semantics Komputationell semantik, 7.5 hecr, part of Master's Programme in Language Technology (MLT)

Summary

In this course we will discuss ways of representing the meaning of words, sentences and discourses in a computer so that such representations can be used in language technology tasks. We start with implementing model theoretical semantics for natural language (as developed for example in Montague semantics) in Python. We also look at theorem proving and its application to reasoning in natural language applications. Finally, we will also look at how meaning can be extracted and modelled distributionally by being extracted from large corpora of text (and images).

Some topics that we will discuss are:

  • models in natural language semantics and their relation to databases and word distributions;
  • ambiguity in natural language and computational solutions in the form of underspecified meaning representations and semantic similarity;
  • inferences in natural language and their modelling in theorem provers and distributional models
  • from semantics of sentences to semantics of discourse.

Course prerequisites:

A passing grade either in:

  • LT2001 Introduction to programming 7.5 HEC
  • LT2002 Introduction to formal linguistics 7.5 HEC
  • LT2003 Natural Language Processing, 15 HEC
  • or equivalent language technology skills and knowledge.

Course syllabus

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Course literature

For a list of suggested general readings please see here. Further, more specific readings will be suggested with each lecture.

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Course materials

Course summary:

Date Details Due