Language and Computation advanced course:
Distributional Semantic Models - Theory and Empirical Results.
Distributional semantic models (DSMs) 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 "in the form of multi-dimensional vector
spaces" through a statistical analysis of the contexts in
which words occur.
With their distributed vector-space representations, DSMs challenge traditional symbolic accounts of conceptual and semantic structures. However, their true ability to address key issues of lexical meaning is still poorly understood, and will have to be carefully evaluated in linguistic and cognitive research.
This course aims to equip participants with the necessary background knowledge for carrying out such research. In addition to the mathematical foundations of DSMs and their application to semantic analysis, we will put particular emphasis on relating the computational models to fundamental issues of semantic theory.