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data:start [2007/11/26 22:35]
marco 		data:start [2010/11/01 14:07] (current)
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-====== Data sets for the evaluation of word space models ====== +====== Resources for the evaluation of word space models ======
- +
-This page contains a developing list of tasks, sub-tasks and corresponding (sub-)data-sets. +
- +
- +
-===== Ordered by task categories ===== +
  
  
 +===== Ordered by events =====
  
 +  * [[:data:esslli2008:start|ESSLLI 2008 Shared Tasks]] (for the [[:workshop:esslli:start|Workshop on Distributional Lexical Semantics]]) 
  
  
 +===== Ordered by task categories =====
  
  
-==== Task 1: Categorization ====+==== Semantic classification ====
  
-Categorization tasks play a prominent role in cognitive research on concepts. In this type of tasks, subjects +  * Noun & verb categorization (ESSLLI 2008) 
-are typically asked to assign experimental items - objects, images, words - +    * [[:data:esslli2008:Concrete Noun Categorization|semantic categories of concrete nouns]] 
-to a given category or to group together items belonging to the same category. +    * [[:data:esslli2008:Abstract/Concrete Nouns Discrimination|abstract vs. concrete nouns]] 
-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.+    * [[:data:esslli2008:Verb Categorization|Levin-style verb classes]]
  
-In the present task, computational models will be tested on their ability to properly group +==== Free association ====
-words into semantic categories. The task is organized into three sub-tasks, focussing on different areas +
-of the lexicon and/or semantic dimensions:+
  
-  * [[Concrete Nouns Categorization]] +  * [[:data:esslli2008:Correlation with Free Association Norms|Correlation with free association norms]] (ESSLLI 2008) 
-  * [[Abstract/Concrete Nouns Discrimination]] +    * discrimination: strong association vs. non-associated 
-  * [[Verb Categorization]]+    * correlation: regression modelling of association strength 
 +    * prediction of most common responses (strongest associations)
  
  
-==== Task 2 ====+==== Property generation ====
  
-  * link to data set +  * [[:data:esslli2008:Comparison with Speaker-Generated Features|Prediction of speaker-generated semantic features]]
-  * link to data set+
  
-==== Task 3: Property Generation ==== 
  
-The ability to describe a concept in terms of its salient properties is an important feature of the 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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