A Wordnet from the Ground Up

132 Chapter 4. Extracting Relation InstancesKennedy (2006) analysed several modified versions of this method. The modificationsconcerned the data in the training/test corpus (varying the positive-to-negativepair ratio and the method of undersampling negative examples) and small differencesin the way of formatting dependency paths. An additional classifiers based on a versionof the Supported Vector Machines algorithm (Joachims, 2002) was applied too,achieving the best F-score 0.633 for a combination of a classifier and filtering basedon Roget’s Thesaurus.Zhang et al. (2006) explored different types of syntactic dependencies at differentlevels of granularity in the construction of classifiers to find occurrences of relationshipsbetween named entities. Five main kinds of relationships with 24 different subtypeswere considered. This approach is broadly similar but the different objective makes acomparison of the results difficult.ML methods of extracting hypernymy pairs usually take lexico-syntactic featuresdirectly to build a classifier. Tens of thousands of features are typical, each carryingvery sparse information. Most of such information “tells” the classifier about variousaspects of semantic relatedness. Features that point to specific lexico-semantic relationsare rare. Section 3.4.5 notes that near-synonyms and close hypernyms/hyponyms of anLU u would be expected close to the top of the list of LUs most semantically related tou, generated by a good MSR. An application of a syntactic analyser is also assumed:a deep parser in (Zhang et al., 2006) or a shallow dependency parser in (Snow et al.,2005, Kennedy, 2006). For many languages such tools are not available yet.We propose to extract hypernymy pairs by relaxing both assumption. There aretwo phases (Piasecki et al., 2008):1. extract the generic relation of semantic relatedness modelled by some MSR,2. identify hypernymy instances – pairs of LUs – from the MSR’s results.The first phase can use all kinds of information that describes the semantics ofLUs, depending on the MSR extraction method. The second phase concentrates ongroups of semantically related LUs and applies specialised tests that distinguish specificlexico-semantic relations as subtypes of semantic relatedness. The tasks of the firstphase are preliminary filtering and problem complexity reduction, so during the secondphase a broader variety of ML methods can be used. An MSR of good accuracy can(by way of its high values) associate LUs that extremely rarely occur close by inthe corpus at hand. Note that such occurrences are the precondition on any patternbasedmethod. MSRs condense information otherwise distributed among many lexicosyntacticpatterns; in phase 2 we can concentrate on the most promising pairs.The only assumption is the availability of a highly accurate MSR. During experimentswe used an MSR based on the Rank Weight Function transformation [MSR RW F ],an earlier version of the Generalised RWF presented in Section 3.4.4. MSR RW F dif-