A Wordnet from the Ground Up

5.1. Weaving the Full-fledged Structure 1675. Selected groups of new lemmas were loaded into WNW and the Algorithm ofActivation-area Attachment [AAA] was run to generate suggestions of attachmentareas.6. Linguists worked freely with the lemma groups; they browsed suggestions in anyorder and edited the wordnet structure.7. At any moment of the process, linguists could re-run AAA to get perhaps bettersuggestions for those new lemmas that have not been edited yet.8. Linguists notified the coordinator about finishing work with particular groups;the coordinator then could analyse the results using the same WNW system(accessing it via the Internet, just like the linguists).The whole process of extracting data sets – sources of evidence for AAA – performedin steps 1-2 took approximately 25 days on a standard PC (3GHz, 4GB RAM,one single-core processor). The time could be reduced to 2-4 days by applying a gridof at least several PCs. This one-time operation is computationally very intensive, butit prepares all data sets except classifiers at the beginning of a long-term expansionprocess. This is done once per each list of new lemmas, independent of the size ofthe list. Classifier training, to be repeated several times with the increasing size of thewordnet, it is much less computationally demanding than the other tasks. AAA is performedon the server, not on the linguists’ PCs. It takes 10-20 minutes on a PC-classserver.Clustering (step 4) is optional from the point of view of the WNW application,which can work efficiently with a list of several thousand new lemmas. Clusteringis necessary for people: a huge flat list is just too difficult to comprehend, and it ispractically impossible to organise around it work lasting several weeks.The idea behind clustering was to divide the initial list into lemma groups in sucha way that each group consists of lemmas with senses belonging to one domain commonto all of them (at least the intersection of the lemma senses should belong to onedomain). There is no perfect clustering algorithm, but manual grouping would be toolabourious to be feasible. We applied an off-the-shelf implementation of clusteringalgorithms in the Cluto package (Karypis, 2002). The input to the clustering algorithmswere values which describe semantic relatedness of lemma pairs acquired fromMSR GRW F . We experimented with different algorithms. After a manual inspectionof the results, we selected graph-based clustering. We did not evaluate the qualityof clustering exhaustively: the mechanism played only a minor, supporting role. Dueto the properties of the clustering algorithms, we repeated the process several times,each time getting some groups and a large set of ‘outliers’, which was next the inputto another run. The obtained groups were loaded into WNW – all in all, 92 groupswere constructed.