A Wordnet from the Ground Up

4.2. Benefits of Handwritten Patterns for Wordnet Expansion 105or(and(equal(cas[0],{nom}),rlook(1,end,$X, inter(flex[$X],{adjectival participles, noun,pronouns, verbal grammatical classes }) ),equal(base[$X],{"być"}),rlook($+1X,end,$Y,or(inter(flex[$Y], {adjectival passive participle,noun, pronouns, verbal grammatical classes }),and( equal(flex[$Y],{prep}),equal(cas[$Y],{inst})))),inter(flex[$Y],{subst,ger,depr}),equal(base[$Y],{"NP2"}),equal(cas[$Y],{inst}),equal(nmb[$Y],nmb[0])),a symmetrical condition for the right context)Figure 4.1: The essentials of the JestInst pattern implementation in JOSKIPIthat they have very similar accuracy. That is why we decided to merge them into acomplex pattern that combines the constraints using the or operator. We will refer tothis pattern as mIInne – see, for example, Table 4.1.4.2 Benefits of Handwritten Patterns for Wordnet ExpansionWe ran experiments on the extraction of hypernymic pairs on the same three corporaas those used for MSR extraction (Section 3.4.5): the IPI PAN Corpus [IPIC](≈ 254 million tokens) (Przepiórkowski, 2004), the Rzeczpospolita corpus [RzCorp](≈ 113 million tokens) (Rzeczpospolita, 2008), and a corpus of large texts in Polishfrom Internet (≈ 214 million tokens) [WebCorp]. Table 4.1 presents detailed resultsfor three patterns, JestInst, NomToNom and mIInne.We assessed the accuracy manually on randomly selected samples. Similarly toother manual evaluations (for example, Section 3.4.5), we determined sample sizesfollowing the method discussed in (Israel, 1992), aiming for the 95% confidence levelon the whole population. We used a program named Sprawdzacz (Kurc, 2008) thatfacilitates manual evaluation of the extracted lexico-semantic relation instances 5 .5 We thank Roman Kurc for his great help with the whole plWordNet project.