A Wordnet from the Ground Up

4.5. Hybrid Combinations 149than some upper-bound distance d h . The distance is simply the number of hypernymylinks to traverse from t. The evidence for the surroundings is treated as less reliablethat for LUs in the central synset t, from the perspective of considering t as the pointof attachment. Any information that describes relations of a new lemma with LUs insynsets other than t is related to t only indirectly, by wordnet links. The weight of thecontext evidence decreases in proportion to the distance.We consider several sources of heterogeneous evidence for a potential relation ofa new lemma with a LU already in the wordnet and thus a relation with some synset.The results of all extraction methods were transformed to sets of LU pairs 〈x, y〉 suchthat x and y are semantically related according to the given method and the corporaanalysed. There are three groups of sets:• two sets produced using MSR RW F – the list MSRlist(y, k) of the k units mostrelated to y, and that list restricted to bidirectional relations:MSR BiDir (y, k) = {y ′ : y ′ ∈ MSRlist(y, k) ∧ y ∈ MSRlist(y ′ , k)};• one set generated by the classifier C H applied to filtering MSRlist(y, k) fromLUs not in hypernymy, meronymy or synonymy with x; C H was trained on thedata from plWordNet;• three sets produced by the manually constructed lexico-syntactic patterns andone set generated by the patterns produced by Estratto.There is only partial overlap among the sources, so we will use them all in expandingthe wordnet. We assume that the subsequent methods explore different pieces ofpartial information available in corpora. We assume, too, that the application of manydifferent methods allows the use of as much lexico-semantic information as possible.Different sources are differently reliable; this can be estimated e.g. by manual evaluationof the accuracy of the extracted pairs. We want to trust the different sources toa different degree: we introduce mechanisms of weighted voting.The algorithm of Activation-area AttachmentThe algorithm is based on the idea of a semantic fit: between two lemmas, as representingtwo LUs linked by a LSR, and between a lemma and a synset, as defininga LU. The fit is identified from all evidence found in corpora. Next, we group synsetswhich fit the input lemma into activation areas, from which the attachment areas areselected and returned. The attachment areas represent LUs which may have differentsenses of the given new lemma; the senses are identified from the input data deliveredto the algorithm.