A Wordnet from the Ground Up

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List of Tables2.1 The size of the core plWordNet . . . . . . . . . . . . . . . . . . . . . 442.2 Average polysemy in the core plWordNet . . . . . . . . . . . . . . . . 452.3 The number of synsets to which a lemma belongs in the core plWordNet 452.4 Sizes of synsets in the core plWordNet . . . . . . . . . . . . . . . . . 452.5 Instances of lexico-semantic relations in the core plWordNet . . . . . 463.1 Changes in synset structure during the development of plWordNet . . 543.2 The accuracy of the MSRs based on the Rank Weight Function algorithm(Section 3.4) in relation to different tests and plWordNet versions(Lemmas – the number of lemmas in QA pairs, QA – the number ofQA pairs for the given test); plWN 1.0 refers to plWordNet, version 1.0 543.3 Results of a manual WBST for Polish verbs and adjectives – the evaluationperformed for (Broda et al., 2008) (May 2007) . . . . . . . . . 573.4 Results of human raters in WBST+H tests generated from the finalversion of the core plWordNet (R — a number of raters for the giventest) . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 573.5 Results of human raters in EWBST (for nominal LUs) generated fromthe final version of the core plWordNet . . . . . . . . . . . . . . . . . 613.6 The accuracy of the lexico-morphosyntactic constraints . . . . . . . . 723.7 GRWF applied to adjectival features for the noun “budynek” (building);frequency is measured in the joint corpus (Section 3.4.5), ‘Lin’ is theweight calculated by the Lin’s algorithm (Lin, 1998) and GRW F Lin– the rank computed by the Generalised RWF function utilising Lin’smeasure. We show 10 most frequent features with their respectivevalues of weight by Lin’s measure and position in ranking created withGRWF. The maximal value of Lin’s weight in the matrix is 6.55. Thehighest rank in GRW F Lin is 8 . . . . . . . . . . . . . . . . . . . . . 753.8 Most important adjectival features for the word budynek ‘building’ . . 753.9 The accuracy of the nominal MSR based on the generalised RWF andLin’s version of MI . . . . . . . . . . . . . . . . . . . . . . . . . . . . 78207
208 List of Tables3.10 The accuracy [%] of nominal MSRs based on different morphosyntacticconstraints; all MSRs use Generalised RWF based on Lin’s MI. “≥ 10 3 ”means more frequent than 1000 . . . . . . . . . . . . . . . . . . . . . 783.11 Examples of lists (G means good) of the 20 LUs most similar to thegiven one according to the MSR based on RWF(z-score) . . . . . . . 813.12 Examples of lists (G – good and Acc – accidental) of the 20 LUs mostsimilar to the given one according to the MSR based on RWF(z-score) 823.13 Experiments with MSRs for frequent lemmas (> 1000 occurrences injoined corpora) . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 833.14 Experiments with MSRs for all lemmas . . . . . . . . . . . . . . . . . 843.15 Manual evaluation of MSR for verbs and adjectives performed for(Broda et al., 2008) on the MSRs from that time . . . . . . . . . . . 853.16 Examples of lists (G — good and Acc — accidental) of the 20 LUsmost similar to the given one for verbs and adjectives . . . . . . . . . 864.1 The results of hypernymy instance extraction by manually constructedlexico-morphosyntactic patterns (No. is the number of LU pairs extracted,Acc. – the accuracy [%], in i patt. – LU pairs occurring in theresults of at least i patterns) . . . . . . . . . . . . . . . . . . . . . . . 1074.2 The influence of the extended reliability measure and changes in thepattern form (“Hum. eval.” – precision based on human judgement,“Ranking” – the number of the top instances above the precision threshold,“Prec. plWN” – precision in relation to plWordNet, “Rel. R” –relative recall relative to ESP-) . . . . . . . . . . . . . . . . . . . . . 1234.3 The dependence of the algorithms on the parameter values (Kurc, 2008) 1244.4 Evaluation for both sets using tenfold cross-validation . . . . . . . . . 1394.5 Precision (P ) and recall (R) [%] of attachment measured during linguist’swork . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 1624.6 The accuracy [%] of plWordNet reconstruction; L – the distance fromthe original synset, S and W mean strong and weak fitness, respectively 1635.1 The size of plWordNet, version 1.0 . . . . . . . . . . . . . . . . . . . 1705.2 Selected counts for Czech, Estonian and German wordnets built in thesecond phase of the EuroWordNet project (Vossen et al., 1999, p. 7) . 1715.3 Average polysemy in plWordNet and PWN 3.0 . . . . . . . . . . . . . 1715.4 The average number of LUs per synset in plWordNet and four EWNwordnets (second phase) (Vossen et al., 1999, p. 7). . . . . . . . . . . 1725.5 Synset sizes. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 1725.6 The number of synsets to which a lemma belongs . . . . . . . . . . . 1745.7 The number of lexico-semantic relations in plWordNet . . . . . . . . . 175
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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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102 Chapter 4. Extracting Relation
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Page 157 and 158: 156 Chapter 4. Extracting Relation
Page 167 and 168: 166 Chapter 5. Polish WordNet Today
Page 186 and 187: Appendix ATests for Lexico-semantic
Page 188 and 189: 187Test for adjectives (T. IX)1. p1
Page 190 and 191: 189RelatednessTest for nouns (T. XV
Page 192 and 193: BibliographyAgarwal, Abhaya and Alo
Page 194 and 195: Bibliography 193on Deep Lexical Acq
Page 196 and 197: Bibliography 195Derwojedowa, Magdal
Page 198 and 199: Bibliography 197Grefenstette, Grego
Page 200 and 201: Bibliography 199Kurc, Roman. (2008)
Page 202 and 203: Bibliography 201Mohammad, Saif and
Page 204 and 205: Bibliography 203. (2006) “The pot
Page 206 and 207: Bibliography 205and Technology 7(1-
Page 210 and 211: List of Figures2.1 The LU perspecti
Page 212 and 213: List of Figures 2114.16 Completely
Page 214 and 215: Index 213CBC, see Clustering by Com
Page 216 and 217: Index 215169, 177, 178, 180, 182hyp
Page 218 and 219: Index 217mutual hypernymy, 24Mutual
Page 220 and 221: Index 219SUMO, 14Supported Vector M
Page 222: A language without a wordnet is at
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A Wordnet from the Ground Up

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