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schtepf [Course description]
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 **Distributional Semantics – A Practical Introduction** **Distributional Semantics – A Practical Introduction**
-[[http://esslli2009.labri.fr/|{{ :course:esslli2009:esslli09_logo.png|ESSLLI ​2009 (Bordeaux)}}]]+[[http://esslli2016.unibz.it/|{{ :course:esslli2018:esslli2016_logo_outline.png?150|ESSLLI ​2016 (Bolzano)}}]] 
 +[[http://​esslli2018.folli.info/​|{{ :​course:​esslli2018:​esslli2018_logo.jpg?​260|ESSLLI 2018 (Sofia)}}]]
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 //​Introductory course at [[http://​esslli2016.unibz.it/?​page_id=242|ESSLLI 2016]], Bolzano, August 15–19, 2016 and [[http://​esslli2018.folli.info/​distributional-semantics-a-practical-introduction/​|ESSLLI 2018]], Sofia, August 6–10, 2018// //​Introductory course at [[http://​esslli2016.unibz.it/?​page_id=242|ESSLLI 2016]], Bolzano, August 15–19, 2016 and [[http://​esslli2018.folli.info/​distributional-semantics-a-practical-introduction/​|ESSLLI 2018]], Sofia, August 6–10, 2018//
   * [[course:​esslli2018:​schedule|Course schedule & handouts]]   * [[course:​esslli2018:​schedule|Course schedule & handouts]]
-  * [[course:​material|Downloads ​important links]] +  * [[course:​material|Software ​data sets]] 
-  * [[course:​bibliography|Suggested readings (bibliography)]]+  * [[course:​bibliography|Bibliography & links]]
 ===== 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://​www.r-project.org/​|R]] software package [[http://​wordspace.r-forge.r-project.org/​|wordspace]] and various pre-built models.
 +**Lecturer:​** [[http://​www.stefan-evert.de/​|Stefan Evert]] (FAU Erlangen-Nürnberg)