A Wordnet from the Ground Up

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4.4. Benefits of Extracted Patterns 127occ=88 rel=0.0060688(hypo:subst:acc) interp który być (hyper:subst:inst)(hypo:subst:acc) interp which is (hyper:subst:inst)The plWordNet-related precision of the Espresso/Estratto algorithm is lower whenmeasured on Polish corpora than the precision reported by Pantel and Pennacchiotti(2006). This might be due to a slightly different approach to precision evaluation,which was performed partially on the basis of the much smaller plWordNet. On theother hand, the results of the manual evaluation are similar to the results reported in(Pantel and Pennacchiotti, 2006). The results for different similarity measures basedon reliability suggest that PMI gives the best results for the given test suite.The adjustment of the pattern scheme to the characteristic features of Polish improvedthe precision over Espresso patterns using only word forms and parts of speechas features.Estratto, the proposed modification of Espresso, succeeded in extracting hypernymyand antonymy from IPIC and the Rzeczpospolita corpus. Attempts to extract meronymywere unsuccessful. Meronymic pairs are present on the MSR-produced list of LUs themost semantically related to the given one, but with failure of the pattern-based attemptswe do not have an additional source of knowledge to separate meronymic pairs fromthose lists.We tested several parameters that have a significant influence on the Estratto algorithm.The most important of them appeared to be:• the number of seed instances,• the confidence threshold,• the number of the top k patterns preserved between the subsequent iterations.The number of seed instances should exceed 10. The confidence threshold stronglydepends on the corpus; for example, for IPIC the best value found was about 2.6. Eachtime the algorithm is applied to a new corpus, both seed instances and the measure ofconfidence must be redefined. The number of the top k patterns should be low around8. Such a number results in a more stable representation of the semantic relation. Itis still unclear how to explore patterns that seem to be correct and are close to thetop. Those patterns usually disappear in the next iterations, so some instances are alsoexcluded from final results.Espresso/Estratto is an intrinsically weakly supervised algorithm. That is true eventhough the preparation of an appropriate set of seeds leading to the extraction of patternsproducing large and diverge set of extracted instances might require even some initial
128 Chapter 4. Extracting Relation InstancesSeed instancessenator (senator)mówca (speaker)nazwa (name)oznaczenie (designation)Polska (Poland)kraj (country)Polska (Poland)państwo (state)wynagrodzenie (remuneration) świadczenie (≈benefit)agencja (agency)jednostka (unit)akademia (academy)uczelnia (university)alkohol (alcohol)substancja (substance)pożar (fire (conflagration)) zdarzenie (event)należność (charge)zobowiązanie (obligation)protokół (protocol)dokument (document)dolar (dollar)waluta (currency)broń (weapon)przedmiot (object)uposażenie (salary)świadczenie (benefit)obligacja (bond)papier (share)zapis (record)dowód (evidence)człowiek (human)podmiot (subject)żywica (resin)spoiwo (adhesive)Extracted instancesszkoła (school)instytucja (institution)maszyna (machine)urządzenie (device)wychowawca (tutor)pracownik (employee)kombatant (veteran)osoba (person)bank (bank)instytucja (institution)pociąg (train)pojazd (vehicle)telewizja (television)medium (medium)prasa (press)medium (medium)szpital (hospital)placówka (institution)czynsz (rent)opłata (payment)grunt (land)nieruchomość (real estate)Wisła (Wisła)rzeka (river)świadectwo (diploma) dokument (document)opłata (payment)należność (charge)ryba (fish)zwierzke (animal)Włochy (Italy)kraj (country)jezioro (lake)zbiornik (reservoir)jarmark (fair)impreza (event)piwo (beer)artykuł (product)zasiłek (dole)świadczenie (benefit)powódź (flood)klęska (disaster)paszport (passport)dokument (document)Figure 4.5: Examples of hypernymy instances extracted by Estratto, version EST+nm
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A Wordnetfrom the Ground Up
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Work financed by the Polish Ministr
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6 Prefaceheartfelt thanks go to all
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8 Chapter 1. Motivation, Goals, Ear
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1.1. Motivation 11[a] special form
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1.1. Motivation 13Affect (Strappara
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1.2. The Goals of the plWordNet Pro
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1.2. The Goals of the plWordNet Pro
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1.3. Early Decisions 19Merge Model:
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1.3. Early Decisions 214. On the ot
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24 Chapter 2. Building a Wordnet Co
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48 Chapter 3. Discovering Semantic
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178 Chapter 5. Polish WordNet Today
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Appendix ATests for Lexico-semantic
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187Test for adjectives (T. IX)1. p1
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189RelatednessTest for nouns (T. XV
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BibliographyAgarwal, Abhaya and Alo
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Bibliography 193on Deep Lexical Acq
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Bibliography 195Derwojedowa, Magdal
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Bibliography 197Grefenstette, Grego
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Bibliography 199Kurc, Roman. (2008)
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Bibliography 201Mohammad, Saif and
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Bibliography 203. (2006) “The pot
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Bibliography 205and Technology 7(1-
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List of Tables2.1 The size of the c
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List of Figures2.1 The LU perspecti
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List of Figures 2114.16 Completely
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Index 213CBC, see Clustering by Com
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Index 215169, 177, 178, 180, 182hyp
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Index 217mutual hypernymy, 24Mutual
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Index 219SUMO, 14Supported Vector M
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A language without a wordnet is at
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A Wordnet from the Ground Up

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