Onto.PT: Towards the Automatic Construction of a Lexical Ontology ...

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86 Chapter 5. Synset Discovery cota and planta. Besides an italian dish (not included), the word pasta might have the popular meaning of money, the figurative meaning of a mixture of things, or it might denote a file or a briefcase. As for the word cota, besides height (not included), it can be a quota or portion, or refer to an old and respectable person, and informally denote a father or a mother. The word planta might either denote a plan or some guidelines, or it might denote a vegetable. Besides some synonyms of plant/vegetable (e.g. planta, vegetal), the synset with the vegetable meaning contains many actual plants or vegetables (e.g. maruge, camélia). After analysing this problem, we noticed that the dictionary DA contains several definitions of plants where the first sentence is just planta, without any differentia. Therefore, even though the correct relation to extract would be hypernymy, our grammars see those definitions as denoting synonymy. Another limitation shown by these examples is that, sometimes, the fuzzy synsets contain words which are not synonyms, but have similar neighbourhoods. 5.3.3 Thesaurus data for different cut points After analysing the fuzzy synsets, we inspected the impact of applying different cut-points (θ) in the transformation of the fuzzy thesaurus into a simple thesaurus. Tables 5.3 and 5.4 present the properties of the different thesauri obtained with different values for θ. Considering just the words of the thesauri, table 5.3 includes the number of words, how many of those are ambiguous, the average number of senses per word, and the number of senses of the most ambiguous word. As for synsets, table 5.4 shows the total number of synsets, the average synset size in terms of words, synsets of size 2 and size larger than 25, which are less likely to be useful (Borin and Forsberg, 2010), as well as the largest synset. Both tables do not consider synsets of size 1. Before collecting the data in Tables 5.3 and 5.4, we followed one of the clustering methods for word senses proposed for EuroWordNet, which suggests that synsets with three members in common can be merged (Peters et al., 1998). However, the design of our clustering algorithm and the configuration of our synonymy networks are prone to create synsets sharing more than one word. So, to minimise the possibility of merging synsets denoting different concepts, we made sure that merged synsets had at least 75% overlap, computed as follows, where |Synset| denotes the number of words of a synset: Overlap(Synseta, Synsetb) = Synseta ∩ Synsetb min(|Synseta|, |Synsetb|) (5.9) As expected, as θ grows, ambiguity drops. This is observed not only from the number of ambiguous words, but also from the average number of word senses and the number of synsets. For instance, with θ = 0.5, despite the establishment of 8,000 clusters, each word has only one sense, which means there is no ambiguity. Out of curiosity, the largest synset in CLIP, with θ = 0.075, denotes the concept of money. It contains the following 58 words: • jimbo, pastel, bagarote, guines, baguines, parrolo, marcaureles, ouro, grana, arame, massaroca, tutu, metal, bagalho, níquel, bilhestres, milho, jan-da-cruz, china, cumquibus, mussuruco, cobre, numerário, pilim, bagaço, pasta, zerzulho, painço, finanças, chelpa, calique, posses, bagalhoça, pecuniária, boro, dieiro, pila, gaita,
5.3. A Portuguese thesaurus from dictionaries 87 Word Concept Fuzzy synsets pasta cota planta money arame(0.6774), zerzulho(0.6774), metal(0.6774), carcanhol(0.6774), pecunia(0.6774), bagarote(0.6774), pecuniária(0.6774), cunques(0.6774), matambira(0.6774), janda-cruz (0.6774), bagalho(0.6774), cacau(0.6774), boro(0.6774), calique(0.6774), marcaureles(0.6774), teca(0.6774), níquel(0.6774), mussuruco(0.6774), massaroca(0.6774), baguines(0.6774), bilhestres(0.6774), parrolo(0.6774), pastel(0.6774), cum-quibus(0.6774), dieiro(0.6774), pilim(0.6774), gimbo(0.6735), chelpa(0.6735), pecúnia(0.6735), patacaria(0.6735), pataco(0.6347), bagalhoça(0.62), bago(0.6181), china(0.6178), cobre(0.6173), numo(0.616), maco(0.5971), jimbo(0.5953), guines(0.5903), pasta(0.5657), maquia(0.5243), gaita(0.5242), grana(0.5226), painço(0.517), jibungo(0.517), numerário(0.5145), dinheiro(0.5139), fanfa(0.4617), posses(0.4604), finanças(0.4425), ouro(0.4259), ... poupanças(0.5202), pé-de-meia(0.289), coscorrinho(0.0799), maquia(0.0698), moenda(0.0648), ... , economia(0.0394), rendor(0.0285), rédito(0.0236), ... ,ganhança(0.0182), lucro(0.015), gimbo(0.0135), chelpa(0.0135), pecúnia(0.0135), patacaria(0.0135), provento(0.0134), arame(0.0133), zerzulho(0.0133), metal(0.0133), carcanhol(0.0133), pecunia(0.0133), cunques(0.0133), pecuniária(0.0133), bagarote(0.0133), matambira(0.0133), jan-da-cruz(0.0133), bagalho(0.0133), cacau(0.0133), boro(0.0133), calique(0.0133), marcaureles(0.0133), teca(0.0133), níquel(0.0133), mussuruco(0.0133), massaroca(0.0133), baguines(0.0133), bilhestres(0.0133), parrolo(0.0133), pastel(0.0133), cum-quibus(0.0133), dieiro(0.0133), pilim(0.0133), pataco(0.0128), bagalhoça(0.0125), bago(0.0125), china(0.0125), cobre(0.0125), numo(0.0125), gage(0.0123), maco(0.0121), jimbo(0.012), ... , guines(0.0119), pasta(0.0114), ... amálgama(0.09279), dossier(0.08130), landoque(0.05162), angu(0.04271), potpourri(0.03949), marinhagem(0.03722), mosaico(0.03648), cocktail(0.03480), mixagem(0.02688), cacharolete(0.02688), macedónia(0.02688), comistão(0.02374), mixture colectânea(0.02317), anguzada(0.02205), caldeação(0.02108), mistura(0.02032), moxinifada(0.01976), imisção(0.01917), massamorda(0.01845), pasta(0.01827), incorporação(0.01800), farragem(0.01779), matalotagem(0.01397), misto(0.01280), file salsada(0.01262), ensalsada(0.01050) diretório(1.0), dossier(0.9176), pasta(0.1118), ... briefcase maleta(0.0759), pasta(0.0128), ... saco(0.0604), maco(0.054), bagalhoça(0.0263), fole(0.0154), ..., mamãe(0.8116), mamã(0.8116), nai(0.7989), malúrdia(0.7989), darona(0.7989), ma- mother mana(0.7989), velha(0.7989), mãe-de-famílias(0.7989), ti(0.7989), mare(0.6503), naia(0.5549), uiara(0.5549), genetriz(0.5549), mãe(0.5221), madre(0.2749), father quota guidelines vegetable cota(0.2407), ... palúrdio(0.6458), dabo(0.6458), genitor(0.6458), painho(0.6458), benfeitor(0.6458), papai(0.6183), papá(0.6169), tatá(0.4934), pai(0.3759), primogenitor(0.3543), velhote(0.2849), velho(0.2817), ... , cota(0.1463), progenitor(0.08416015), ascendente(0.062748425) colecta(0.6548), quota(0.5693), contingente(0.309), pagela(0.2304), prestação(0.1723), cota(0.1655), mensalidade(0.0908), quinhão(0.0605),... prospectiva(0.5166), prospecto(0.0805), prospeto(0.0663), calendarização(0.0595), prisma(0.055), óptica(0.055), programa(0.0452), planos(0.0354), intuitos(0.0354), traçado(0.034), traçamento(0.0295), olhar(0.0284), perspectiva(0.0271), gizamento(0.0261), alçado(0.0258), horizonte(0.0228), planificação(0.0228), visualidade(0.0227), gázeo(0.0227), panorama(0.0213), calendário(0.0206), aspeito(0.0176), ... , conspecção(0.017), programação(0.0168), desenho(0.0164), terrapleno(0.0157), diagrama(0.0152), fácies(0.0149), ângulo(0.0145), estampa(0.0141), esquema(0.0141), contenença(0.0134), duaire(0.0133), duairo(0.0133), arquitectura(0.0128), probabilidade(0.0127), vista(0.0126), viseira(0.0124), design(0.0116), faceta(0.0113), janela(0.0112), alinhamento(0.0112), abordagem(0.011), desígnio(0.0107), planta(0.0107), painel(0.0105), projecto(0.0104) mapam(0.4176), gráfico(0.4176), organigrama(0.2037), mapa(0.0512), tábua(0.0417), carta(0.0359), catálogo(0.0339), planta(0.0227), procedência(0.0225) gizamento(0.1106), planificação(0.0483), programação(0.0357), terrapleno(0.0332), diagrama(0.0322), traçamento(0.0313), esquema(0.0298), arquitectura(0.0271), design(0.0247), programa(0.0187), desenho(0.0174), rascunho(0.0169), idéia(0.0161), plana(0.0142), traçado(0.0141), prospecto(0.0129), planta(0.0114) plantas(0.65226775), marrugem(0.53500473), caruru-guaçu(0.4826975), planta(0.325316), caruru(0.21026045), marugem(0.19776152), vinagreira(0.15554681), vegetal(0.14959434), murugem(0.0829055), bananeirinha-do-brejo(0.06475778), pranta(0.038034387), camélia(0.030711966), alçado(0.02910556), traçado(0.027653778), cordão-de-san-francisco(0.024771051), maruge(0.024124833), rosa-dojapão(0.023929605), japoneira(0.023929605), cordão(0.021231819), melancia(0.015556527), presentação(0.011791914), condicionamento(0.011791912), ervaferro(0.011321816) Table 5.2: Fuzzy synsets of polysemic words.
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PhD Thesis Doctoral Program in Info
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Preface About six years ago, almost
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Resumo Não há grandes dúvidas qu
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Contents Chapter 1: Introduction .
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8.2.1 Semantic Web model . . . . .
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6.1 Illustrative synonymy network.
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6.3 Evaluation against intersection
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Chapter 1 Introduction A substantia
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1.2. Approach 5 • They are not bu
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1.4. Outline of the thesis 7 which
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Chapter 2 Background Knowledge The
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2.1. Lexical Semantics 11 that, in
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2.1. Lexical Semantics 13 Meronymy
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2.2. Lexical Knowledge Formalisms a
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2.3. Information Extraction from Te
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2.3. Information Extraction from Te
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2.4. Remarks on this section 25 usi
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28 Chapter 3. Related Work in group
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30 Chapter 3. Related Work ple rela
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32 Chapter 3. Related Work knowledg
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34 Chapter 3. Related Work the ELRA
Page 54 and 55: 36 Chapter 3. Related Work resource
Page 56 and 57: 38 Chapter 3. Related Work English
Page 58 and 59: 40 Chapter 3. Related Work of super
Page 60 and 61: 42 Chapter 3. Related Work • part
Page 62 and 63: 44 Chapter 3. Related Work LSIE fro
Page 64 and 65: 46 Chapter 3. Related Work modifier
Page 66 and 67: 48 Chapter 3. Related Work 6. {,}
Page 68 and 69: 50 Chapter 3. Related Work 1. Extra
Page 70 and 71: 52 Chapter 3. Related Work Due to t
Page 72 and 73: 54 Chapter 3. Related Work comparis
Page 74 and 75: 56 Chapter 3. Related Work creation
Page 76 and 77: 58 Chapter 4. Acquisition of Semant
Page 98 and 99: 80 Chapter 5. Synset Discovery Ther
Page 100 and 101: 82 Chapter 5. Synset Discovery the
Page 102 and 103: 84 Chapter 5. Synset Discovery tb-t
Page 106 and 107: 88 Chapter 5. Synset Discovery θ W
Page 108 and 109: 90 Chapter 5. Synset Discovery Tabl
Page 110 and 111: 92 Chapter 5. Synset Discovery word
Page 113 and 114: Chapter 6 Thesaurus Enrichment Gene
Page 115 and 116: 6.1. Automatic Assignment of synpai
Page 117 and 118: 6.2. Evaluation of the assignment p
Page 119 and 120: 6.3. Clustering and integrating new
Page 121 and 122: 6.4. A large thesaurus for Portugue
Page 129: 6.5. Discussion 111 Another contrib
Page 132 and 133: 114 Chapter 7. Moving from term-bas
Page 149 and 150: Chapter 8 Onto.PT: a lexical ontolo
Page 151 and 152: 8.1. Overview 133 items inside a sy
Page 153 and 154: 8.2. Access and Availability 135 no
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8.2. Access and Availability 137 Ex
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8.3. Evaluation 139 Figure 8.3: Ins
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8.3. Evaluation 141 the most reliab
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8.3. Evaluation 143 imation of the
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8.3. Evaluation 145 Relation parteD
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8.4. Using Onto.PT 147 • S: (n) a
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8.4. Using Onto.PT 149 todos os fun
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8.4. Using Onto.PT 151 In addition
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8.4. Using Onto.PT 153 based approa
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8.4. Using Onto.PT 155 Uma populaç
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158 Chapter 9. Final discussion 3.
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160 Chapter 9. Final discussion - G
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162 Chapter 9. Final discussion Any
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References Agichtein, E. and Gravan
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References 167 for storing and quer
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References 169 15th International C
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References 171 Symposium (STAIRS 20
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References 173 Hovy, E., Hermjakob,
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References 175 ACM, 38(11):39-41. M
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References 177 ACL Press. Partee, B
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References 179 Russell, S. and Norv
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References 181 Proceedings of 13th
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Appendix A Description of the extra
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• x propriedadeDeAlgoQueCausa y -
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• x antonimoAdjDe y Property - x
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190 Appendix B. Coverage of EuroWor
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