Distributional Semantics – A Practical Introduction

Language and Computation Courses

Introductory Course

Distributional Semantics – A Practical Introduction,
Stefan Evert (Friedrich-Alexander-Universität Erlangen-Nürnberg, Germany)

Distributional semantic models (DSM) – also known as “word space” or “distributional similarity” models – 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 high-dimensional vector spaces – 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.

This course aims to equip participants with the background knowledge and skills needed to build different kinds of DSM representations – from traditional “count” models to neural word embeddings – and apply them to a wide range of tasks. There will be a particular focus on practical exercises with the help of user-friendly software packages and various pre-built models.