Onto.PT: Towards the Automatic Construction of a Lexical Ontology ...
Onto.PT: Towards the Automatic Construction of a Lexical Ontology ...
Onto.PT: Towards the Automatic Construction of a Lexical Ontology ...
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126 Chapter 7. Moving from term-based to synset-based relations<br />
% <strong>of</strong> N Algorithm Precision (%) Recall (%) F1(%) F0.5(%) RF1(%)<br />
RP 89.1 61.5 72.8 81.7 85.7<br />
AC 93.4 78.0 85.0 89.9 93.4<br />
RP+AC 93.1 78.4 85.1 89.9 93.1<br />
50% NT 89.2 72.7 80.1 85.4 89.2<br />
NT+AC 94.1 76.3 84.3 89.9 94.1<br />
PR 57.3 55.0 56.1 56.9 57.3<br />
MD 64.6 69.6 67.0 65.5 64.6<br />
RP 88.5 40.9 55.9 71.8 71.1<br />
AC 82.8 70.4 76.1 80.0 82.8<br />
RP+AC 84.1 72.5 77.9 81.5 84.1<br />
25% NT 82.0 65.6 72.9 78.1 82.0<br />
NT+AC 77.6 73.2 75.3 76.7 77.6<br />
PR 55.7 54.0 54.8 55.3 55.7<br />
MD 58.5 67.7 62.7 60.1 58.5<br />
RP 87.6 29.2 43.8 62.6 58.9<br />
AC 76.8 66.0 71.0 74.4 76.8<br />
RP+AC 79.0 70.3 74.4 77.1 79.0<br />
12.5% NT 74.8 56.2 64.2 70.2 74.8<br />
NT+AC 63.4 73.0 67.9 65.1 63.4<br />
PR 53.3 53.4 53.4 53.4 53.3<br />
MD 49.0 64.6 55.7 51.5 49.9<br />
Table 7.5: Results <strong>of</strong> ontologising 800 antonymy tb-triples, between nouns, <strong>of</strong> TeP<br />
in TeP, using only part <strong>of</strong> <strong>the</strong> TeP’s antonymy relations as a lexical network.<br />
% <strong>of</strong> N Algorithm Precision (%) Recall (%) F1(%) F0.5(%) RF1(%)<br />
RP 95.2 76.3 84.7 90.7 95.2<br />
AC 92.7 82.3 87.2 90.4 92.7<br />
RP+AC 96.6 82.8 89.2 93.5 96.6<br />
50% NT 93.5 79.2 85.8 90.2 93.5<br />
NT+AC 94.9 80.1 86.9 91.5 94.9<br />
PR 51.3 56.5 53.8 52.3 51.3<br />
MD 79.1 78.0 78.6 78.9 79.1<br />
RP 93.8 61.0 73.9 84.7 89.6<br />
AC 93.5 82.3 87.5 91.0 93.5<br />
RP+AC 94.5 82.3 88.0 91.8 94.5<br />
25% NT 91.2 77.0 83.5 88.0 91.2<br />
NT+AC 94.2 80.9 87.0 91.2 94.2<br />
PR 51.4 56.5 54.8 52.4 51.4<br />
MD 75.6 76.3 75.6 75.7 75.6<br />
RP 93.0 47.6 63.0 78.1 82.7<br />
AC 88.6 78.0 83.0 86.2 88.6<br />
RP+AC 89.9 79.2 84.2 87.6 89.9<br />
12.5% NT 87.5 71.8 78.8 83.8 87.5<br />
NT+AC 88.0 79.2 83.4 86.1 88.0<br />
PR 51.3 55.7 53.4 52.1 51.3<br />
MD 70.2 74.9 72.4 71.1 70.2<br />
Table 7.6: Results <strong>of</strong> ontologising 800 antonymy tb-triples, between verbs, <strong>of</strong> TeP<br />
in TeP, using only part <strong>of</strong> <strong>the</strong> TeP’s antonymy relations as a lexical network.<br />
result in uniformly distributed missing tb-triples. What happens when extracting<br />
information from text is that some parts <strong>of</strong> <strong>the</strong> network might be almost complete,<br />
while o<strong>the</strong>r parts, possibly those with less frequent words and relations, will be<br />
almost incomplete.