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data:abstract_concrete_nouns_discrimination [2008/01/20 16:50]
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-====== Task 1.b - Abstract/Concrete Nouns Discrimination ====== 
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-==== Introduction ==== 
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-The contrast between abstract and concrete words plays a central role in human cognition. Actually, behavioural and neuropsychological evidence suggests that abstract and concrete concepts might be represented, retrieved and processed differently in the human brain. 
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-Since semantic classifications of abstract nouns have a higher degree of arbitariness than the ones for concrete nouns, we have not defined any a priori "ontology" of classes for the abstract domain. Instead, we will test computational models for their ability to discriminate between abstract and concrete nouns. 
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-The {{concabst.dataset.tar.gz |data set}} consists of 40 nouns extracted from the [[http://ota.ahds.ac.uk/textinfo/1054.html|MRC Psycholinguistic Database]], with rates by human subjects on the concreteness scale.  
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-==== Task Operationalization ==== 
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-The nouns have been classified into three classes: 
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-  * **HI** - 15 nouns selected from those in MRC with the highest concreteness value. These are a subset of the nouns in the data set for the [[http://wordspace.collocations.de/doku.php/data:concrete_nouns_categorization|concrete noun categorization task]]; 
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-  * **LO** - 15 nouns selected from those in MRC with the lowest concreteness value (e.g. "hope"); 
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-  * **ME** - 10 nouns selected from those in MRC whose concreteness socre is close to the average (e.g. "pollution", "fight"). 
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-We operationalize the abstract/concrete noun discriminaion as a 2-way clustering task of the 30 nouns in the dataset belogingin to the HI and LO classes. 
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-To abstract away from differences stemming from any specific clustering method, you are asked to run your experiments with the //k-means// algorithm available in [[http://glaros.dtc.umn.edu/gkhome/cluto/cluto/overview|CLUTO]]. In case you can not run  [[http://glaros.dtc.umn.edu/gkhome/cluto/cluto/overview|CLUTO]] on your system, the workshop organizers will carry out the clustering for you. In this case, data should be prepared according to a format that will be specified later on. Participants are also invited to experiment with other clustering methods and to compare the results with those obtained with[[http://glaros.dtc.umn.edu/gkhome/cluto/cluto/overview|CLUTO]]. 
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-Back to [[Start]] 
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