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

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146 Chapter 8. Onto.PT: a lexical ontology for Portuguese acto, ...). Another important difference regards the size and the correction of the synsets of MWN.PT and Onto.PT. The former contains small synsets, often with only one word, while the latter, as referred earlier, contains large synsets. On the other hand, given its manual revision, the MWN.PT synsets are supposedly all correct, while Onto.PT, due to its automatic construction, contains incorrections. 8.4 Using Onto.PT The main goal of creating Onto.PT is its exploitation in the achievement of tasks on the computational processing of Portuguese. As referred earlier in this chapter, this is also a popular approach to validate ontologies (Brank et al., 2005). In order to illustrate the utility of a resource as Onto.PT, in this section, we provide utilisation scenarios, where this resource can be seen as a valuable contribution. All the scenarios intend to be mere proofs of concept. None of the used techniques are very sophisticated and we did not go further on their evaluation. We start by presenting an exercise on exploring the taxonomy of Onto.PT. Then, we show how Onto.PT can be applied to word sense disambiguation (WSD). After that, we briefly describe how this resource was integrated in an information retrieval (IR) system, in order to enhance query expansion. The IR system was evaluated with the participation in an IR joint task. The last utilisation scenario is about taking advantage of Onto.PT to answer cloze questions automatically. 8.4.1 Exploring the Onto.PT taxonomy The first usage example is a simple exploration exercise, showing that, besides providing synonyms for lexical items, Onto.PT can be queried to acquire taxonomic information, as well as other semantic information on the organisation of the lexicon. Figure 8.5 shows a four-level taxonomy obtained from Onto.PT, where cão (dog) is included. For the sake of simplicity, we omit some synset entries, as well as non-hypernym relations, from this figure. The taxonomy shows that Onto.PT can be used, for instance, to collect a list of animals, a list of mammals, or a list of dog breeds. Starting with the most general level, with a synset denoting an animal, its is possible to obtain kinds of animals, including birds (ave), insects (insecto), and mammals (mamífero). Mammals can be expanded for obtaining mammal synsets, including cow (vaca), whale (baleia), cat (gato), or dog (cão). Finally, if the hypernyms of the dog synset are expanded, several dog breeds are shown, including boxer, mongrel (rafeiro) or dalmatian (dálmata). 8.4.2 Word sense disambiguation There is a wide range of knowledge-based WSD algorithms, using a wordnet both as sense inventory and as an additional source of knowledge (e.g. Resnik (1995); Banerjee and Pedersen (2002); Agirre and Soroa (2009)). As Onto.PT is structured in a similar fashion to a wordnet, most of the previous algorithms may be adapted to use Onto.PT for performing Portuguese WSD. We have implemented two algorithms for this task: Bag-of-Words and Personalized PageRank (Agirre and Soroa, 2009).
8.4. Using Onto.PT 147 • S: (n) animal, bicho, balada, piolho, béstia, alimal, minante • [hiperonimoDe] – ... – S: (n) ave, ribeirinhas, sabacuim, volátil – S: (n) micróbio, bactéria, microorganismo, bacilo, microrganismo – S: (n) insecto, inseto, xerimbabo – ... – S: (n) mamífero, mamíferos, mastozoário – [hiperonimoDe] ∗ ... ∗ S: (n) vaca, seminarista, vaquinha ∗ S: (n) baleia ∗ S: (n) gato, grampo, tareco, narro ∗ S: (n) rata, rato, ratazana, toupeira ∗ S: (n) cão, cachorro, cã, narro, calote, perro, mísula, au-au, adia, bêlfo, jaguara, cátulo ∗ [hiperonimoDe] · ... · S: (n) boxer · S: (n) rafeiro, vira-lata · S: (n) galgo, lebreiro, lebrel · S: (n) perdigueiro · S: (n) dálmata · S: (n) poodle, caniche · S: (n) buldogue · S: (n) labrador, labradorite · S: (n) pastor-alemão · S: (n) dobermann · S: (n) são-bernardo · S: (n) husky · S: (n) bigle · ... ∗ S: (n) castor ∗ S: (n) tigre, tigrinho, tigrino ∗ S: (n) golfinho, golfim, germão ∗ S: (n) raposa, volpe, tamarança ∗ S: (n) leopardo, pardo, pantera ∗ S: (n) lince ∗ S: (n) onça, jaguar, onça-pintada, leopardo-das-neves ∗ S: (n) canguru ∗ S: (n) gorila, gorilha ∗ S: (n) morcego, pacó, guembo ∗ S: (n) javali, porco-montês, porco-bravo ∗ S: (n) veado, cervo, corço, enho ∗ S: (n) gazela ∗ S: (n) foca, vítulo, arctocéfalo, boi-marinho ∗ S: (n) dromedário ∗ S: (n) panda ∗ S: (n) lontra, ratão-d’água, nútria ∗ S: (n) girafa, camelopárdale ∗ ... – S: (n) micro-organismo – ... 0.9 Figure 8.5: Search example: breeds of dog in Onto.PT.
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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
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36 Chapter 3. Related Work resource
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38 Chapter 3. Related Work English
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40 Chapter 3. Related Work of super
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42 Chapter 3. Related Work • part
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44 Chapter 3. Related Work LSIE fro
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46 Chapter 3. Related Work modifier
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48 Chapter 3. Related Work 6. {,}
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50 Chapter 3. Related Work 1. Extra
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52 Chapter 3. Related Work Due to t
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54 Chapter 3. Related Work comparis
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56 Chapter 3. Related Work creation
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58 Chapter 4. Acquisition of Semant
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80 Chapter 5. Synset Discovery Ther
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84 Chapter 5. Synset Discovery tb-t
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86 Chapter 5. Synset Discovery cota
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88 Chapter 5. Synset Discovery θ W
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90 Chapter 5. Synset Discovery Tabl
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92 Chapter 5. Synset Discovery word
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Page 119 and 120: 6.3. Clustering and integrating new
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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
Page 155 and 156: 8.2. Access and Availability 137 Ex
Page 157 and 158: 8.3. Evaluation 139 Figure 8.3: Ins
Page 159 and 160: 8.3. Evaluation 141 the most reliab
Page 161 and 162: 8.3. Evaluation 143 imation of the
Page 163: 8.3. Evaluation 145 Relation parteD
Page 167 and 168: 8.4. Using Onto.PT 149 todos os fun
Page 169 and 170: 8.4. Using Onto.PT 151 In addition
Page 171 and 172: 8.4. Using Onto.PT 153 based approa
Page 173: 8.4. Using Onto.PT 155 Uma populaç
Page 176 and 177: 158 Chapter 9. Final discussion 3.
Page 178 and 179: 160 Chapter 9. Final discussion - G
Page 180 and 181: 162 Chapter 9. Final discussion Any
Page 183 and 184: References Agichtein, E. and Gravan
Page 185 and 186: References 167 for storing and quer
Page 187 and 188: References 169 15th International C
Page 189 and 190: References 171 Symposium (STAIRS 20
Page 191 and 192: References 173 Hovy, E., Hermjakob,
Page 193 and 194: References 175 ACM, 38(11):39-41. M
Page 195 and 196: References 177 ACL Press. Partee, B
Page 197 and 198: References 179 Russell, S. and Norv
Page 199 and 200: References 181 Proceedings of 13th
Page 201 and 202: Appendix A Description of the extra
Page 203 and 204: • x propriedadeDeAlgoQueCausa y -
Page 205: • x antonimoAdjDe y Property - x
Page 208 and 209: 190 Appendix B. Coverage of EuroWor
Page 210 and 211: 192 Appendix B. Coverage of EuroWor
Page 212: 194 Appendix B. Coverage of EuroWor
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