A Wordnet from the Ground Up

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3.5. Sense Discovery by Clustering 99171 committee: {karanie punishing, leczenie treatment, prewencja prevention, profilaktykaprophylaxis, rozpoznawanie diagnosing, ujawnianie revealing, wykrywaniediscovering, zapobieganie preventing, zwalczanie fighting, ściganie pursuing,prosecuting} – a correct additional sense found196 committee: as aboveNext, two examples of committees and the generated word groups.• committee 57: {ciemność darkness, cisza silence, milczenie silence = not speaking}• LU group: {cisza, milczenie, ciemność, spokój quiet, bezruch immobility, samotnośćsolitude, pustka emptiness, mrok dimness, cichość silence (literary), zadumareverie, zapomnienie forgetting, nuda ennui, tajemnica secret, otchłań abyss,furkot whirr, skupienie concentration, cyngiel trigger, głusza wilderness, jasnośćbrilliance}• committee 69: {grota grotto, góra mountain, jaskinia cave, lodowiec glacier,masyw massif, rafa reef, skała rock, wzgórze hill}• LU group: {góra, skała, wzgórze, jaskinia, masyw, pagórek hillock, grota,wzniesienie elevation, skałka small rock, wydma dune, górka small mountain,płaskowyż plateau, podnóże foothill, lodowiec, wyspa island, wulkan volcano,pieczara cave, zbocze slope, ławica shoal}Finally, an example of a polysemous committee and the lemma group generated onthis basis. The group clearly consists of two separate parts: animals and zodiac signs.• committee 11: bestia beast, byk bull, lew lion, tygrys tiger• LU group: {lew, byk, tygrys, bestia, wodnik aquarius, koziorożec capricorn,niedźwiedź bear, smok dragon, skorpion scorpio, nosorożec rhinoceros, bliźniętwin, lampart leopard, bawół buffalo}The last examples clearly show the role of the committee in defining the mainsemantic axis of the LU group. Two general semantically different LUs in the samecommittee make it ambiguous between at least two senses. Such a committee resultsin inconsistent LU groups created from it. Thus the initial selection of committees iscrucial for the quality of the whole algorithm, and the quality of CBC depends directlyon the MSR applied.Although the experiments performed with CBC gave interesting and promisingresults, we achieved too low accuracy for using the output (i.e. the groups of words)
100 Chapter 3. Discovering Semantic Relatednessas a direct help in expanding plWordNet (not to mention treating the groups as synsetproposals). Nevertheless, we plan to improve the CBC accuracy even further in thefuture, and use it as one additional knowledge source for the semi-automatic expansionof plWordNet.
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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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150 Chapter 4. Extracting Relation
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166 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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