Differences
This shows you the differences between two versions of the page.
Next revision | Previous revision Last revision Both sides next revision | ||
course:esslli2018:start [2018/07/25 10:26] schtepf created |
course:esslli2018:start [2018/07/26 09:24] schtepf [Distributional Semantics – A Practical Introduction (ESSLLI 2016 & 2018)] |
||
---|---|---|---|
Line 2: | Line 2: | ||
**Distributional Semantics – A Practical Introduction** | **Distributional Semantics – A Practical Introduction** | ||
- | [[http://esslli2009.labri.fr/|{{ :course:esslli2009:esslli09_logo.png|ESSLLI | + | [[http://esslli2016.unibz.it/|{{ :course:esslli2018:esslli2016_logo_outline.png?150|ESSLLI |
+ | [[http:// | ||
\\ | \\ | ||
// | // | ||
* [[course: | * [[course: | ||
- | * [[course: | + | * [[course: |
- | * [[course: | + | * [[course: |
===== Course description ===== | ===== Course description ===== | ||
+ | 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. It is accompanied by practical exercises with the user-friendly [[http:// |