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course:esslli2018:start [2018/07/25 11:41]
course:esslli2018:start [2018/07/26 09:25] (current)
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.
 +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)