A Wordnet from the Ground Up

130 Chapter 4. Extracting Relation InstancesIt seems to be helpful to allow merging such patterns, maybe like this:(hypo:subst:case1) i inny (hyper:subst:case1).The results for ESP- and EST-, where there are no such strict constraints, suggestsome increase in recall. Another way, much more complicated, is to enrich the patternrepresentation, so that additional syntactic information (at least about nominal LUs)can be used.The list of acquired instances cannot be directly imported to plWordNet. First ofall, the list is flat. There is no information on synsets. The percentage of erroneousLU pairs on the lists (such as 63% for EST+nm) is too high to trust the list as sourceof automatic expansion of the plWordNet hypernymy structure. Also, many positiveLU pairs represent in fact quite remote hypernymic links.These observations show the drawbacks, but there also are pluses. EST+nm extracted3700 hypernymic LU pairs (37% of the 10000 LU pairs). This informationcan be combined with MSR G RW F , producing higher values for wordnet relation instances.The MSR alone does not say what kind of relation made two LU closelysemantically related. The information acquired by Estratto sheds light on this issue.Section 4.5.3 presents a fairly succesful algorithm based on this reasoning. A manualcomparison of the LU pairs extracted by Estratto and the three manual patterns revealsthat both sets are disjoint to some extent. We noted earlier that manual patterns aremore expressive and can find hypernymic instances in language construction which areinaccessible for the present Estratto patterns. This can be changed in the future extensionsof Estratto, but for now we used both types of patterns in the hybrid algorithmof plWordNet expansion in Section 4.5.3.4.5 Hybrid Combinations: Patterns, Distributional Semanticsand ClassifiersWe noted at the end of Section 3.4.5 that Measures of Semantic Relatedness [MSRs]can recognize semantically related LUs with an accuracy approaching human performance.Still, MSRs produce lists of the k LUs most semantically related to the givenLU x [MSRlist (x,k) ] with few instances of wordnet relations, and they do not knowhow to distinguish the direction of a relation. We named two ways of compensatingfor these drawbacks: introduce a classifier operating on MSRlists (x,k) , capable ofdifferencing relations, or combine a MSR with other sources of knowledge, includinglexico-syntactic patterns or the existing wordnet structure. This subsection willexamine both possibilities.