A Wordnet from the Ground Up

3.5. Sense Discovery by Clustering 87possibility of constructing an MSR significantly better in this respect. Semantic andpragmatic constraints make many LUs semantically related to many other LUs andMRSs based on distributional semantics generate a continuum of relatedness valuesfor pairs of LUs. Wordnet relations appear as just weakly identifiable characteristicsubspaces in the continuum of semantic relatedness. We need an additional way ofselecting those LU pairs from the MSRlist (x,k) lists which represent particular wordnetrelations. Two ways appear to emerge:1. application of lexico-syntactic patterns (Sections 3.2 and 4) as an additionalsource of knowledge,2. introduction of an additional classifier trained on the plWordNet data and used forfiltering out MSRlist (x,k) pairs which are not instances of any wordnet relation(Section 4.5.1).We also mentioned briefly in the discussion of verbal and adjectival MSRs theidea of changing the perspective from automatic extraction of sets of instances of thewordnet relations to expanding the existing wordnet with new lemmas anchored inexisting synsets. This can significantly extend the amount of knowledge available andreduce the complexity of the problem. We will present in Section 4.5.3 a solutionfollowing this idea. Let us emphasise here that it is automated wordnet expansionwhich was our assumed goal, not automatic wordnet construction from scratch.3.5 Sense Discovery by ClusteringThe synset is one of the most fundamental building blocks of the wordnet structure.An algorithm for automatic extraction of synsets would be very helpful for linguistswho build a wordnet up manually (though usually with substantial software support).Clustering groups objects on a hyperplane so as to minimise the distance betweenobjects inside a group and maximise the distance between objects from different groups.A definition of distance, or similarity, between objects is required for such grouping.For clustering of lemmas into synset-like groups, we could use directly a Measure ofSemantic Relatedness [MSR] (Section 3.4). A drawback would be that MSRs tend tomerge different lemma senses in one vector that represents the meaning of lemmas,or to over-represent one predominant sense of a given lemma (Piasecki et al., 2007a).That is why we need a clustering method aware of ambiguity in lemma meaning.The Most Frequent Sense heuristic states that in one genre or domain one senseof a given lemma is dominant (Agirre and Edmonds, 2006). Without thematicallylabelled corpora one can hope that clustering techniques make it possible to achieveapproximation of domains, because documents are grouped by similarity. On the other