This is an old revision of the document!

# Data sets for the evaluation of word space models

It is tempting to make a connection between the statistical association patterns of words – both first-order associations (collocations) and higher-order associations (word space) – and human free associations – the first words that come to mind when native speakers are presented with a stimulus word. In this task, we will explore to what extent such free associations can be explained and predicted by statistically salient patterns in the linguistic experience of speakers, possibly offering a simple and straightforward cognitive interpretation of distributional similarity (i.e. higher-order association). However, this is not merely a “baseline” task: it also touches on intriguing research problems such as the interaction of first-order and higher-order information in human associative memory.

NB: On March 29th, we fixed a small (but serious) bug in script eval_task3.perl. If you obtained a copy at an earlier time, please download the most recent version of the package and use it for your evaluation.

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.

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: