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course:esslli2018:start [2018/07/25 11:41]
schtepf 		course:esslli2018:start [2018/07/26 09:25]
schtepf [Course description]
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   * [[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.
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 +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.
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 +**Lecturer:** [[http://www.stefan-evert.de/|Stefan Evert]] (FAU Erlangen-Nürnberg)

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