A Wordnet from the Ground Up

3.5. Sense Discovery by Clustering 91After the creation of clusters, the weight w is assigned using χ 2 test for testingif there is a bias in occurrences of the lemma with the group. Within our approach,a weight for a lemma is calculated in a way combining the methods of Indyka-Piasecka(2004) and Matsuo and Ishizuka (2004):w l = α · mintf.idf l+β · cv l + γ · χ 2 l , (3.8)where min tf.idfl is the minimal tf.idf weight for the given term l across the documentsin a cluster, α, β, γ are parameters controlling impact of every measure on final weight.Words which are assigned the highest weights are used as labels for the group ofdocuments in the cluster tree.3.5.2 Benefits of document clusters for constructing a wordnetOur ultimate goal in document clustering was to obtain the basic structure for plWord-Net. Document group labels could be used as synsets and cluster tree as a hypernymyhierarchy.We evaluated our approach on plWordNet. The automatically created thesaurus wascompared with the plWordNet hypernymy hierarchy. This failed: only 86 hypernymicinstances (word pairs) were present in the thesaurus, fewer than 1% of all relations.Clustering whole documents might be a reason of low accuracy, but experiments withdocument segmentation decreased the quality of clustering (Broda, 2007, Broda and Piasecki,2008a). On the other hand, keyword extraction methods developed primarilyfor information retrieval are not suitable for the discovery of relations between wordsthat describe different groups of documents.The extracted group labels are still quite very descriptive. For example, a group ofdocuments about “interventionist purchase of grain and harvest in the area of Małopolska”are labelled with zboże (grain), pszenica (wheat), tona (tonne), rolnik (farmer)and agencja (agency). Another possible use of extracted words is to measure the degreeof polysemy, because different meanings of words occurs in different branches ofhierarchy.3.5.3 Clustering by Committee as an example of word sense discoveryA good MSR can provide valuable information about word similarity during wordnetconstruction. For every word x, an MSR can produce a list of its k most similar words(denoted as MSRlist(x, k)) . Because of the nature of MSRs, those lists consists notonly of words related by one lexico-semantic relation (Section 3.4). Part of the wordson those similarity lists can be even unrelated to the target word. Choosing the rightvalue for k can also be problematic. Not only does it depend on the MSR algorithm,