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course:esslli2018:start [2018/07/26 09:06] schtepf [Course description] |
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| 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. | 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:// | + | 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:// |
