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

More documents

Recommendations

Info

128 Chapter 7. Moving from term-based to synset-based relations Algorithm Adjectives (800 tb-triples) Precision (%) Recall (%) F1(%) F0.5(%) RF1(%) RP 99.4 40.8 57.8 77.2 87.0 AC 62.0 50.2 55.5 59.2 62.0 RP+AC 69.3 69.7 69.5 69.4 69.3 NT 70.2 21.1 32.5 48.0 50.9 NT+AC 51.6 77.9 62.0 55.3 51.6 PR 50.5 61.4 55.4 52.4 50.5 MD 60.0 74.9 66.6 62.4 60.0 Table 7.9: Results of ontologising 800 antonymy tb-triples, between adjectives, of TeP in TeP, using all CARTÃO as an external lexical network N. When there is not enough information, the tb-triple is simply not ontologised. The algorithms AC and, especially, RP+AC, have more balanced results in all measures, and are more in agreement with the evaluation of the other types of relation, in section 7.2.2. This happens because, although the network is incomplete, AC uses other relations to compensate the lack of information on relations of the same type. We can say that AC is more tolerant to missing information. We should finally remind that the antonymy relation is not very prototypical because it connects concepts with an opposite meaning. Antonyms are similar in all contextual properties but one (see more about antonymy in Murphy (2003)). Furthermore, the previous evaluation showed that the best algorithm was dependent on the semantic relation. Therefore, we cannot make a blind extrapolation of these results to a real scenario, where there are other types of relations, and the lexical network is frequently incomplete. 7.3 Discussion This chapter presented several algorithms for ontologising tb-triples, which attach related term arguments to suitable synsets in a wordnet-like resource. In our case, this resource does not contain any explicit semantic relations between synsets. Therefore, the proposed algorithms use nothing besides the synsets themselves and a set of provided tb-triples, which establish a lexical network. If the lexical network contains all the extracted information, which is what happens in the creation of Onto.PT, the former can be seen as the extraction context. We have obtained interesting results but, as other authors referred (e.g. Pennacchiotti and Pantel (2006)), we have confirmed that ontologising is a challenging task. Other alternatives should be devised, so we leave some ideas for further experiments: • To ontologise, manually, a representative set of tb-triples (the gold reference might be a starting point) or, automatically, the tb-triples in which confidence is very high. Then, exploit the resulting sb-triples to ontologise the remaining tb-triples in a new algorithm, or learn a classifier for this task automatically; • We did not conclude if there was any especially noisy (or useful) type of relation in the application of the cosine similarity, so we have used them all. More experiments should be done to analyse this deeper; • Investigate if we can take advantage of the occurrence of tb-triples in textual corpora to increase or decrease the confidence of an attachment;
7.3. Discussion 129 • The gold reference should be enlarged, not just in terms of tb-triples, but also in terms of covered relations, in order to have more significant results. Nevertheless, when attaching hypernymy, part-of and purpose-of tb-triples, all algorithms precisions outperform the random chance. Given the obtained results, in the creation of Onto.PT, we decided to use AC for ontologising the hypernymy tbtriples (almost one third of CARTÃO) and RP+AC for ontologising the rest of the tb-triples. These choices are mainly supported by the precision and RF measures. Experimentation using the antonymy relations of TeP, a handcrafted gold resource, were also performed. Although this resource only contains sb-triples, these experimentations confirmed that RP+AC is the best performing algorithm. Only when the lexical network is complete, RP seems to perform better. But this is not the typical case, and it is definitely not the case in the creation of Onto.PT, because the lexical network is automatically extracted from dictionaries and thus larger and incomplete. In the next chapter, we present the current version of Onto.PT. It results from the application of the automatic step described here for ontologising the tb-triples extracted from dictionaries (CARTÃO network, see chapter 4), in the enriched TeP (TRIP thesaurus, see chapter 6).
Page 1:
PhD Thesis Doctoral Program in Info
Page 5:
Preface About six years ago, almost
Page 9 and 10:
Resumo Não há grandes dúvidas qu
Page 11 and 12:
Contents Chapter 1: Introduction .
Page 13:
8.2.1 Semantic Web model . . . . .
Page 16 and 17:
6.1 Illustrative synonymy network.
Page 18 and 19:
6.3 Evaluation against intersection
Page 21 and 22:
Chapter 1 Introduction A substantia
Page 23 and 24:
1.2. Approach 5 • They are not bu
Page 25 and 26:
1.4. Outline of the thesis 7 which
Page 27 and 28:
Chapter 2 Background Knowledge The
Page 29 and 30:
2.1. Lexical Semantics 11 that, in
Page 31 and 32:
2.1. Lexical Semantics 13 Meronymy
Page 33 and 34:
2.2. Lexical Knowledge Formalisms a
Page 35 and 36:
Page 37 and 38:
Page 39 and 40:
2.3. Information Extraction from Te
Page 41 and 42:
2.3. Information Extraction from Te
Page 43:
2.4. Remarks on this section 25 usi
Page 46 and 47:
28 Chapter 3. Related Work in group
Page 48 and 49:
30 Chapter 3. Related Work ple rela
Page 50 and 51:
32 Chapter 3. Related Work knowledg
Page 52 and 53:
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 78 and 79:
Page 80 and 81:
Page 82 and 83:
Page 84 and 85:
Page 86 and 87:
Page 88 and 89:
Page 90 and 91:
Page 92 and 93:
Page 94 and 95:
Page 96 and 97: 78 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 104 and 105: 86 Chapter 5. Synset Discovery cota
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
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 and 164: 8.3. Evaluation 145 Relation parteD
Page 165 and 166: 8.4. Using Onto.PT 147 • S: (n) a
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:
Page 212:
show all

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

You also want an ePaper? Increase the reach of your titles

Delete template?

Save as template?