Task 1.a - Concrete Noun Categorization

Introduction

The goal of the sub-task is to group concrete nouns into semantic categories.

The data set consists of 44 concrete nouns, belonging to 6 semantic categories (four animates and two inanimates). The nouns are included in the feature norms described in McRae et al. (2005) (cf. Task3).

Task Operationalization

We operationalize concrete noun 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:

  • 6-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”), tool (“hammer”), vehicle (“car”);
  • 3-way clustering - models will be tested on their ability to categorize the nouns into 3 classes: animal (superordinate of bird and groundAnimal), vegetable (superordinate of fruitTree and green), and artifact (superordinate of tool and vehicle);
  • 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)

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 CLUTO. In case you can not run 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 withCLUTO.

Task Evaluation

Evaluation will be carried in two stages:

1. quantitative evaluation - results will be evaluated with respect to the two measures for cluster quality available in CLUTO: purity and entropy (cf. Zhao, Y. and G. Karypis (2002), “Evaluation of Hierarchical Clustering Algorithms for Document Datasets”, in CIKM 2002).

2. qualitative evaluation - participants will be asked to perform a fine-grained error analysis, focussing on critical nouns, hard classes, etc. Details about this type of evaluation will be provided later on.

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