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data:concrete_nouns_categorization [2008/01/19 18:03]
alexlenci 		— (current)
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-====== Task 1a: Concrete Nouns Categorization ====== 
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-==== Introduction ==== 
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-The goal of the sub-task is to group concrete nouns into semantic categories. 
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-The {{concnouns.categorization.dataset.tar.gz |data set}} consists of 44 concrete nouns, belonging to 7 semantic categories (four animates and two inanimates). The nouns are included in the feature norms described in McRae et al. (2005) (cf. [[comparison_with_speaker-generated_features|Task3]]). 
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-==== Task Operationalization ==== 
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-We operationalize concrete nouns categorization as a clustering task. Since the data set is organized hierarchically, 
-we will run three clustering experiments, varying the number of classes and consequently their level of generality: 
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-  * **7-way clustering** - models will be tested on their ability to categorize the nouns into the most fine-grained classes of the dataset: //bird// ("peacock"), //groundAnimal// ("lion"), //fruitTree// ("cherry"), //green// ("potato"), //kitchenware// ("spoon"), //instrument// ("hammer"), //vehicle// ("car"); 
-  * **4-way clustering** - models will be tested on their ability to categorize the nouns into 4 superordinate classes: //animal// (superordinate of //bird// and //groundAnimal//), //vegetable// (superordinate of //fruitTree// and //green//), //tool// (superordinate of //kitchenware// and //instrument//), //vehicle//; 
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-  * **2-way clustering** - models will be tested on their ability to categorize the nouns into the two top classes: //natural// (superordinate of //animal// and //vegetable//) and //artifact// (superordinate of //tool// and //vehicle//) 
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-To abstract away from differences stemming from the particular clustering algorithm, you are asked to run your experiments with k-means algoriothm available in [[http://glaros.dtc.umn.edu/gkhome/cluto/cluto/overview|CLUTO]]. In case you can not use LUTO on your syste, you can provide us with your mdeols 
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-Particpnats ore obvioulty free to experiemnt also with tor ownn favoiurteio model. 
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-==== Task Evaluation ==== 
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-We envisage e two stage evaluation: 
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-1. quantitative evaluation - clustering results wil be evaluated with repect to the two standrd meuare used in CLUTO: cluter purity and cluster entropy (for details see??).  
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-2. qualitative evaluation - particpnats will be asked to focus on a fine-grained process of error analysis, to identify the hardeest nouns to cluster, etc.  
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-Back to [[Start]] 
  

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