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:

Across disciplines, researchers are eager to gain insight into empirical features of abstract vs. concrete concepts and words. In the first part of this course, we present an overview of the cognitive science literature which reports extensive analyses of how concrete concepts are processed, with however little consensus about the nature of abstract concepts. In the second part of this course, we look into this dichotomy from a computational perspective, where the inclusion of information regarding the concreteness of words has been demonstrated to play a key role across NLP tasks, such as the automatic identification of figurative language. Additionally, we describe and discuss the procedures of collecting human-generated ratings of abstractness and their usage for both communities. Overall, this course thus aims at introducing and discussing cognitive and computational resources and empirical studies in order to understand the role and application of abstractness in large-scale data-driven models.