Index 213CBC, see Clustering by Committeechunker, 102, 108, 112classification problem, 131classifier, 50, 131–135, 138–143, 146, 147,166classifier-based approach, 50closehypernym, 148, 152hypernymy, 92, 148hyponym, 148hyponymy, 148clustering, 18, 36, 87, 153Clustering by Committee, 88, 91–100clustering ofdocuments, 88, 91lemmas, 87, 90, 92, 166–168lexical units, 48, 50, 143clustering-based approach, 49, 50co-hyponym, 42, 53, 58, 79, 135, 141,147, 155co-incidence matrix, 63, 65, 67, 72, 76,80, 94, 104, 133, 135, 136co-occurrence matrix, see co-incidencematrixCo-occurrence Retrieval Model, 74Cohen’s kappa, 56collocation, 66concept, 7–10, 13, 18, 20, 24, 96conceptual relation, 10conceptualization, 20, 21constraint, 62context, 8, 10, 11, 20, 23, 50, 62, 149–152, 178conversion, 26–28, 46, 175, 186coordination of adjectives, 71coordinator, 37, 41, 44, 167, 169core plWordNet, 16, 36, 39, 40, 42, 44–47,51, 56, 57, 59, 61, 62, 64, 77, 80,85, 104, 106, 112, 119, 120, 144,163, 165, 169, 172, 176–179CoreNet, 14corpus, 12, 16–18, 36, 47–50, 63, 64, 67,71, 73, 74, 76–78, 87, 101–103,105, 107, 108, 118–122, 125–127,129, 131, 133, 136–138, 141, 143,147, 150, 154, 165–166, 170, 176,180, 183cosine measure, 51, 74, 144cousin, 79cousins, 146–147CRM, see Co-occurrence Retrieval ModelCRM with Mutual Information, 80Croatian WordNet, 19cross parts-of-speech near antonymy, 10cue validity, 90cut-off precision, 120, 126Czech WordNet, 20, 33, 38DanNet, 14, 19data driven approach, 166DEBVisDic, 38, 42decision tree, 138, 139, 144, 145dependency parser, 64derivation, 21, 33derivational relation, 10, 24, 32, 40, 175,181derivative, 32–34deterministic parser, 64detractor, 52, 58, 60dictionary-based approach, 77diminutive form, 33, 34directhypernym, 145, 147, 152, 178hyponym, 147, 178, 180direct hypernymy, 53distributional association, 48Distributional Hypo<strong>the</strong>sis, 49, 61, 62Distributional Semantics, 17, 18, 144, 145distributional similarity, 49
214 Indexdocumentfrequency, 90hierarchy, 88segmentation, 91similarity, 87English, 11, 20, 21, 32, 52, 95, 102, 113,115–117, 181Enhanced WBST, 52, 56, 58, 78, 98entailment, 9Espresso, 112–113, 127–129Estratto, 102, 113, 127–130, 149, 166, 183EuroWordNet, 9, 10, 12–15, 18–21, 32,38, 48, 171–172evaluation, 37, 50, 154, 155, 163evaluation bycomparison, 50comparison with human judgement,51<strong>the</strong> direct comparison, 51<strong>the</strong> indirect comparison, 51evaluation of clustering, 89EWBST, see Enhanced WBSTEWN, see EuroWordNetexpand model, 19FarsNet, 14feature, 65, 67, 71–76, 80, 83, 93–95, 97,98filtering, 73Fleiss’s kappa, 56flexible word-order, 95, 113, 115folk knowledge, 13FrameNet, 13frequent lexical unit, 56, 60, 61fuzzynymy, 26, 34, 35, 46, 175Generalised RWF, 75–76, 132, 166, 167generic pattern, 107, 108, 114, 115, 118,121–123, 129GermaNet, 30, 39, 145, 181gerunds, 169Global WordNetAssociation, 13, 14Conference, 11, 13, 14Grid, 14, 15, 181gloss, 7, 11, 13, 18, 20, 21, 40, 51Google, 129grammaticalcategory, 17, 32, 70, 72, 117, 118,121class, 36, 68, 69, 104, 117relation, 61, 62granularity, 62graph-based clustering, 167Growing Hierarchical Self-Organising Map,89–90GRWF, see Generalised RWFhas subevent relation, 9heuristic voting, see weighted votingHindi WordNet, 14holonym, 24, 25, 32, 35, 79, 148, 182holonymy, 9, 25–27, 31, 32, 46, 79, 175,183, 188Hungarian WordNet, 14, 19hybrid approach, 50hypernym, 16, 17, 25, 28, 29, 31, 35, 38,41–43, 79, 103, 104, 106, 117,125, 126, 130–146, 148, 151, 152,155, 156, 160, 176, 179hypernymy, 7, 9, 11, 12, 16, 17, 20, 21,23, 25, 26, 28–30, 38, 46, 58,79, 101–103, 106, 107, 112, 115,119, 122, 129, 131, 132, 136,137, 141, 147, 149, 151–153, 155,175, 177, 178, 180, 182, 187distance, 59, 153, 159hierarchy, see hypernymy structurestructure, 11, 12, 20, 30, 36–38, 45,51, 53, 58, 59, 91, 107, 130, 131,134, 143–148, 153, 155, 163, 168,
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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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