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

6.4. A large thesaurus for Portuguese 105
Properties of the synonymy networks
In a similar fashion to what was done for the complete network (table 5.1), table 6.6
contains the total number of nodes (|V |) and edges (|E|), and the average network
degree (deg(N), computed according to expression 5.5). It contains as well the
number of sub-networks (Sub-nets), which are group of nodes connected directly
or indirectly in N; the number of nodes of the largest and second largest subnetworks
(|Vlcs| and |Vlcs2|); and the average clustering coefficient of the largest
sub-network (CClcs, computed according to expression 5.7).
From table 6.6, we notice that these synonymy networks are significantly different
from the original. First, they are smaller, as they only contain about half of the
nouns, one sixth of the verbs and one third of the adjectives. Second, they have
substantially lower degrees, and clustering coefficients close to 0, which means they
are less connected and do not tend to form clusters. Nevertheless, they still have
one large core sub-network and several smaller.
This confirms that a simpler clustering algorithm is suitable for our purpose,
especially because ambiguity is much lower and several clusters are already defined
by complete small sub-networks. The noun network contains 4,470 sub-networks of
size 2 and 1,127 of size 3. These numbers are respectively 437 and 97 for verbs, and
1,303 and 262 for adjectives.
POS |V | |E| deg(N) Sub-nets |Vlcs| CClcs |Vlcs2|
Noun 21,272 15,294 1.44 6,556 2,816 0.03 66
Verb 1,807 1,197 1.32 614 153 0.00 29
Adjective 4,695 3,050 1.30 1,743 169 0.02 50
Table 6.6: Properties of the synonymy networks remaining after assignment.
Clustering Examples
Figures 6.2, 6.3 and 6.4 illustrate the result of clustering in three sub-networks. The
first sub-network results in only one cluster, with several synonyms for someone
who speaks Greek. The second and the third are divided into different clusters,
represented by different shades of grey.
In figure 6.3, the sub-network is divided in two different meanings of the verb
’splash’, one of them more abstract (esparrinhar), and the other done with the
feet or hands (bachicar), but three words may be used with both meanings. The
meanings covered by the four clusters in figure 6.4 are, respectively: a person who
gives moral qualities; a person who evangelises; a person who spreads ideas; and a
person who is an active member of a cause.
Evaluation of the clustering results
In order to check if the algorithm described in section 6.3 is efficient, and to have an
idea on the quality of the discovered clusters, their manual evaluation was performed.
Once again, we had two judges classifying pairs of words, collected from the same
synset, as synonymous or not. This kind of evaluation is easier and slightly less
subjective than the evaluation of complete synsets. Furthermore, in section 5.3.5
we reported similar results using both kinds of evaluation.