A Wordnet from the Ground Up

144 Chapter 4. Extracting Relation Instanceshypernymy structure is normally a graph. In any case, no automatic method can comeup with a credible top portion of a wordnet hierarchy. That is why the top levels ofplWordNet’s hypernymy hierarchy have been built manually, and we defined the focusof our research as a semi-automatic expansion of the core plWordNet. The constructedcore plWordNet then serves as the springboard for what have turned out to be usefulsuggestions of attaching new lemmas to particular synsets in plWordNet. Such lemmaswould be attached as related to, but not necessarily synonymous with, LUs in thosesynsets.Several projects have explored the idea of building an expanded wordnet over anexisting one. Most of them are focused, and have been tested, only on PWN. Theadvantage is the possibility of using the wordnet structure already in place, especiallythe hypernymy structure, as a knowledge source.Caraballo (1999, 2001) discusses an interesting attempt to overcome those problems.In her approach, the meaning of nouns is described simultaneously in two ways.In a distributional semantics model, for each noun a vector is constructed with theco-occurrence frequencies of this noun and other nouns in coordinate and appositiveconstructions. The frequencies are collected from parsed text. In a pattern-basedmodel, hypernym pairs are extracted by Hearst’s pattern (Hearst, 1992) X, Y, andother Zs. The vectors give a cosine-measure similarity of nouns and noun clusters.A binary tree of clusters is built following the scheme of agglomerative clustering.Next, internal tree nodes are assigned hypernyms of the branches by extracting fromthe pattern-based pairs the most frequent hypernyms of the LUs in the given branch.Finally, the binary tree is “compressed” by removing internal nodes that have no hypernymsassigned or represent the same hypernyms as their parent node. A manualevaluation of a randomly selected sample showed that on average 33% of nouns wereassigned correctly as hyponyms of the examined hypernyms. The sample was verysmall and not representative, and a 33% precision is similar to the precission achievedin our experiments on pattern-based hypernym extraction. Carraballo’s approach, whileinteresting, required parsing (a drawback if no good parser is available) and was appliedto a limited domain of economy and texts from the Wall Street Journal. The achievedprecision seems limited and directly correlated with the precision of the patterns, andthe constructed hierarchy is far from the wordnet synset structure: the number ofinternal nodes is small in comparison to the number of leaf clusters and their largesize.Alfonseca and Manandhar (2002) assigned to synsets a meaning representationbased on distributional semantics model, and treated the hypernymy structure labelledin that way as a kind of decision tree. To find a site for a new lemma, the treeis traversed top-down each time, choosing a branch with the highest distributionalsimilarity. The top-level synsets were mostly very general, so they introduced a limitedpropagation of meaning vectors from children to parents.