A Wordnet from the Ground Up

More documents

Recommendations

Info

4.5. Hybrid Combinations 139ratio was randomly reduced to 1:1 (the uniform distribution of probability was appliedin drawing a new subset NH).P R F 1 P R F 1ratio 1:1 1:10Naïve BayesH+P2,P3+R 89.80 47.10 61.79 46.30 45.80 46.05H+P2,P3+R+E 84.70 59.10 69.62 34.60 53.50 42.02C4.5H+P2,P3+R 82.10 77.50 79.73 66.90 43.10 52.43H+P2,P3+R+E 81.70 78.40 80.02 60.70 39.90 48.15LMTH+P2,P3+R 81.80 80.60 81.20 72.80 39.40 51.13H+P2,P3+R+E 81.00 78.20 79.58 65.40 34.50 45.17Table 4.4: Evaluation for both sets using tenfold cross-validationPrecision and recall are calculated in Table 4.4 according to the description ofexamples extracted from plWordNet (H, P2, P3, R) or defined manually (E). Theresults achieved by both decision trees are very similar, and high by all three measures.However, the inclusion of the set E decreases the result significantly in comparisonto the high ratio |R| : |E|, that is to say, a small number of more difficult examplesnegatively influence the result. The R set includes more obvious and on average lessclosely semantically related pairs of LUs; it is generated randomly from plWordNet,but E includes only tricky examples. That is why we ran additional tests on a separateset of LU pairs selected randomly from MSRlists(a, 20) generated using MSR RW F .The set was annotated manually, and will be referred to as the manual test set (M).The best classifiers shown in Table 4.4 appeared to be biased towards positive decision,contrary to the classifiers trained on the 1:10 version of the learning data.In Figure 4.6 we present sample results of the classification selected from oneof the folds of the tenfold cross-validation (classifier C4.5, ratio KH to NH 1:10, Eincluded in NH) 13 .We prepared the set M in order to go outside plWordNet with the tests and to lookinto the work of the classifiers from the point of view of their potential application inlinguistic practice. As we wrote earlier, the set M was selected randomly from pairs ofLUs with the highest value of semantic relatedness according to MSR RW F . M consistsof 1984 negative and 316 positive examples.The C4.5 classifier trained on the KH=H+P2 and NH=P3+R+E with the ratio 1:10achieved a 21.69% precision, a 50.32% recall and a 30.31% F-score. This is a littlelower than the best result achieved by (Snow et al., 2005) using corpus-based attributes13 Many words in these pairs are polysemous in both languages. The English translations “select” theintended meaning.
140 Chapter 4. Extracting Relation InstancesTrue positivesakt (act)ustawa (bill)bank (bank)firma (firm)emocja (emotion)smutek (sadness)intelekt (intellect)przymiot (attribute)licencja (licence)zezwolenie (permission)pragnienie (desire)ochota (willingness)terytorium (territory)kolonia (colony)warzywo (vegetable)kartofel (potato)False positivescelnik (customs officer)policja (police)czynsz (rent)oprocentowanie (interest)dochód (income)dotacja (donation)nonszalancja (nonchalance)rozrzutność (profligacy)odpad (waste)produkt (product)problem (problem)rodzina (family)temat (topic)dostarczyciel (provider)zachwyt (admiration)zdumienie (astonishment)True negativesczłowieczeństwo (humanity) prorok (prophet)licencja (licence)zarządzenie (regulation)opis (description)hipoteza (hypothesis)ślub (wedding)kochanek (lover)tempo (speed)sport (sport)trybunał (tribune)sejm (diet (parliament))False negativeslinia (line)ogonek (queue)konstrukcja (construction)twierdza (fortress)nieprzychylność (unfriendly attitude) emocja (emotion)podpora (support)kula (sphere)zakochanie (infatuation)emocja (emotion)Figure 4.6: Example results of the classification of LU pairs acquired from plWordNet as relevant (nearsynonymsand close hypernyms) and not relevant. The classifier was C4.5, the positive tonegative ratio 1:10; manually prepared negative examples were used together with automaticallygenerated examples
Page 1 and 2:
A Wordnetfrom the Ground Up
Page 3 and 4:
Work financed by the Polish Ministr
Page 7 and 8:
6 Prefaceheartfelt thanks go to all
Page 9:
8 Chapter 1. Motivation, Goals, Ear
Page 12 and 13:
1.1. Motivation 11[a] special form
Page 14 and 15:
1.1. Motivation 13Affect (Strappara
Page 16 and 17:
1.2. The Goals of the plWordNet Pro
Page 18 and 19:
1.2. The Goals of the plWordNet Pro
Page 20 and 21:
1.3. Early Decisions 19Merge Model:
Page 22:
1.3. Early Decisions 214. On the ot
Page 25 and 26:
24 Chapter 2. Building a Wordnet Co
Page 27 and 28:
Page 29 and 30:
Page 31 and 32:
Page 33 and 34:
Page 35 and 36:
Page 37 and 38:
Page 39 and 40:
Page 41 and 42:
Page 43 and 44:
Page 45 and 46:
Page 47 and 48:
Page 49 and 50:
48 Chapter 3. Discovering Semantic
Page 51 and 52:
Page 53 and 54:
Page 55 and 56:
Page 57 and 58:
Page 59 and 60:
Page 61 and 62:
Page 63 and 64:
Page 65 and 66:
Page 67 and 68:
Page 69 and 70:
Page 71 and 72:
Page 73 and 74:
Page 75 and 76:
Page 77 and 78:
Page 79 and 80:
Page 81 and 82:
Page 83 and 84:
Page 85 and 86:
Page 87 and 88:
Page 89 and 90: 88 Chapter 3. Discovering Semantic
Page 103 and 104: 102 Chapter 4. Extracting Relation
Page 139: 138 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 208 and 209:
List of Tables2.1 The size of the c
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
show all

A Wordnet from the Ground Up

Create successful ePaper yourself

Delete template?

Save as template?