The organizers will provide a list of word-senses corresponding to WordNet 2.1.

In a small period of time, the participants should return a set of Topic Signatures (lists of words and associated weights) corresponding to the list of word-senses provided by the organizers. The format of the Topic Signatures should follow the format of the trial data-set.

Each participant should provide a zip or tar.gz file containing a set of files. Each file should contain a Topic Signature for a particular word-sense (that is, those words that better characterize the word-sense). The filename should follow the format word#pos#sense (for instance, party#n#1) indicating the word-sense of WordNet 2.1.

Each file corresponding to a word sense will include a word weighted list of terms. Each line will correspond to a word with its associated weight (which could represent the weight of the relation between the word and the word-sense).

All knowledge resources submitted for the task will be indirectly evaluated on a common Word Sense Disambiguation task. In particular, we will use the English Lexical Sample framework of SemEval-07 (task 17, subtask 1). That is, using a limited number of words and annotated test examples for their word senses. All performances will be evaluated on the test data using the scoring system provided by the Lexical Sample task organizers.

Furthermore, trying to be as neutral as possible with respect to the semantic resources submitted, we will apply systematically the same disambiguation method to all of them. Recall that our main goal is to establish a fair comparison of the knowledge resources rather than providing the best disambiguation technique for a particular semantic knowledge base.

A simple word overlapping counting (or weighting) will be performed between the Topic Signature and the test example. We will also consider multiword terms. Thus, the occurrence evaluation measure will count the amount of overlapping words and the weight evaluation measure will add up the weights of the overlapping words. The synset having higher overlapping word counts (or weights) will be selected for a particular test example.

The participants, having wide-coverage semantic resources associated to WordNet, will receive a list of word senses. In a very short time, they should submit the appropriate Topic Signatures for the corresponding word senses.

We will also stablish a set of baselines using existing wide coverage semantic resources such as WordNet, MCR, etc.