Mathias Winther Madsen

Week 1, August 3-7, 2020

Abstract:

Information theory is a branch of probability theory that investigates the fundamental limits on our ability to communicate reliably. By quantifying the concepts of uncertainty and information in a mathematically meaningful way, it allows us to formulate precise results about the amount of information we can hope to transmit in the presence of noise, and evaluate whether a code achieves this maximal efficiency. These results have concrete applications for the design of communication systems, but also profound consequences for linguistics and epistemology.

Stefan Evert and Gabriella Lapesa

Week 2, August 10-14, 2020

Abstract:

Distributional semantic models (DSM) are based on the assumption that the meaning of a word can (at least to a certain extent) be inferred from its usage, i.e. its distribution in text. Therefore, these models dynamically build semantic representations through a statistical analysis of the contexts in which words occur. DSMs are a promising technique for solving the lexical acquisition bottleneck by unsupervised learning, and their distributed representation provides a cognitively plausible, robust and flexible architecture for the organisation and processing of semantic information.

Diego Frassinelli and Sabine Schulte Im Walde

Week 2, August 10-14, 2020

Abstract:

We consider the three major approaches to semantics that can be distinguished by their formal apparatus: formulaic, geometric, and algebraic and argue that these are trunk, leg, and tail of the same elephant.