A Wordnet from the Ground Up

4.5. Hybrid Combinations 143eliminated, but the error of elimination is small. Even so, we are still rather far froma support tool truly valuable from the linguists’ point of view.Because of the small size of plWordNet, it will be a laborious process to prepare amore demanding training set. In the case of each LU pair we can suspect that it is notyet described in plWordNet – building the set means expanding the wordnet. Nonetheless,it can be done and some bootstrapping approach can be applied in improving theclassifier and expanding the wordnet. The next section presents work along these lines.In contrast with (Snow et al., 2005), who use directly lexico-syntactic features, weproposed a two-step approach. It is intrinsically based on MSR, on whose quality itsomewhat depends. On the other hand, a good MSR can introduce a general descriptionof relations among LUs and deliver knowledge derived from a very large number ofcontexts, not only direct LU co-occurrences. The complex attributes designed forthe classifiers are a form of pre-processing. They express condensed information thatfacilitates the classifiers’ decision processes. The results achieved on the manual testset M shows that the present set of attributes does not give enough evidence fordistinguishing near-synonyms and close hypernyms from co-hyponyms. More researchis necessary on other possible sources of knowledge.4.5.3 Multicriteria voting in wordnet expansionA wordnet is built of LUs, synsets and relation links. After a rather unsuccessfulattempt to acquire lemmas for LUs from corpora (Section 2.4), we took an initialbatch from a small dictionary (Piotrowski and Saloni, 1999). We tackled the extractionof wordnet relation instances several times. We considered Measures of SemanticRelatedness [MSRs] (Section 3.4), manually constructed patterns (Section 4.1), automaticallyextracted patterns (Section 4.3) and a classifier-based method (Section 4.5.1).We have not achieved results better than around 30% of accuracy, but many symptomssuggest that a combination of algorithms can improve the accuracy a lot. In this sectionwe will investigate thoroughly this possibility. The extraction of synsets, on the otherhand, seems to be a serious problem. We could notice this in Section 3.5. The bestclustering algorithm produces interesting results,but is still far from being a source ofautomatically extracted synsets. Clustering of LUs is a self-organising process andtherefore raises expectations which in our case have not been met. From the pointof view of future wordnet user one would expect, if not directly synsets, than somegeneral but intuitively distinguished and useful classes as represented by the higherlevels of the hypernymy structure.Clustering algorithms also tend to produce a flat set of clusters. Changing such a setof clusters into a hierarchy poses two problems: how to identify the right shape of thetree and how to label higher levels of the cluster tree with the adequately general LUs.Moreover, most hierarchical clustering methods produces strict trees, while a wordnet