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data:start [2008/01/20 19:19] marco |
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====== Data sets for the evaluation of word space models ====== | ====== Data sets for the evaluation of word space models ====== | ||
- | This page contains a developing list of tasks, sub-tasks and corresponding (sub-)data-sets. | ||
- | Other tasks or sub-tasks might be added in the near future. | + | ===== Ordered by events ===== |
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+ | * [[: | ||
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- | ==== Task 1: Free Association | + | ==== Semantic classification |
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- | It is tempting to make a connection between the **statistical association** patterns of words -- first-order (// | + | |
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- | * [[Correlation with Free Association Norms]] | + | |
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- | ==== Task 2: Categorization ==== | + | |
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- | Categorization tasks play a prominent role in cognitive research on concepts. In this type of tasks, subjects | + | |
- | are typically asked to assign experimental items - objects, images, words - | + | |
- | to a given category or to group together items belonging to the same category. | + | |
- | Since categorization presupposes an understanding of the relationship between the items in a category, it is regarded as a key source of evidence on the organization and structure of the human conceptual system. | + | |
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- | In the present task, computational models will be tested on their ability to properly group | + | |
- | words into semantic categories. The task is organized into three sub-tasks, focussing on different areas | + | |
- | of the lexicon and/or semantic dimensions: | + | |
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- | * [[Concrete Noun Categorization]] | + | |
- | * [[Abstract/ | + | |
- | * [[Verb Categorization]] | + | |
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- | ==== Task 3: Property Generation ==== | + | |
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- | The ability to describe a concept in terms of its salient properties is an important feature of human conceptual cognition. In this task, we compare human-generated //norms// collected by psychologists to the properties generated by computational models. | + | |
- | * [[Comparison with Speaker-Generated Features]] | + | |
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+ | * [[: | ||
+ | * [[: | ||
+ | ==== Free association ==== | ||
+ | * [[: | ||
+ | * discrimination: | ||
+ | * correlation: | ||
+ | * prediction of most common responses (strongest associations) | ||
+ | ==== Property generation ==== | ||
+ | * [[: | ||
- | ===== Source corpus ===== | ||
- | You can train your word space on your favorite corpus. However, we also invite you, if this is suitable, to experiment with the [[http:// | ||