# Graph-Based Taxonomic Semantic Class Labeling
## 4 Results and Discussion
For instance, the first semantic class with various philosophers is labeled dynasty.n.01, while the very abstract semantic class _standards-n_, _values-n_, etc. yields the even more abstract synset label _be-lier_.\(n\).01. This exemplifies the overall validity of providing hypernym synsets for taxonomic semantic labeling. One of the main advantages of the WordNet hypernym graph algorithm is the symbolic categorical assignment of lexical nodes to a class within a structured taxonomy. This allows semantic enrichment of the associated lexical communities obtained by unsupervised bottom-up graph classification method and results in a set of synsets with well-defined and curated top-down knowledge relations. The hypernym graph \(F[hyper]_{i}^{s}\) abstracts the categories of lexical communities using WordNet dictionary knowledge relative to the data provided by a large web corpus. This results in a comparable corpus-based representation of lexical usage given the same set of graph parameters. For example, Table 7 shows the labeling of communities based on the English Timestamped newsfeed 2014-2019 corpus. Both corpora yield a set of sense clusters that abstract _anger-n_ in a comparatively similar sense of distinct strong feeling _emotion.n.01_, associated in particular with _violence, insecurity, intolerance, resentment_, and _sadness_. Another advantage of using WordNet is the ability to find corresponding hypernym structures in many languages via the Open Multilingual WordNet library [12]. The results of the comparative translation equivalent of the concept _anger-n_ in Croatian, concept _ljutnja-n_, calculated on the basis of the Croatian hrWac corpus (hrWac) [58; 59] are presented in Table 8.  
The cross-linguistic comparison yields a commensurable and yet culturally specific insight into the associative conceptual matrix of the lexeme _ljutnja_, which indicates a somewhat different picture than its English translation equivalent. The lexeme _ljutnja_ in hrWac also displays the aggressive features of anger, but is more abstractly associated with states and emotions experienced when one is not well or does not achieve the desired goal, as well as with the quality of having no strength or power. We can argue that corpus-based taxonomic labeling graph procedures highlight usage-based and cultural differences in the semantic processing of the same lexical concept. These \begin{table}
\begin{tabular}{l l l} \hline \hline  & **Associated Community** & **WordNet Hypernym Labels** \\ \hline  & disappointment-n, disgust-n, shock-n, & collapse.n.01, reflex.n.01, fight.n.02, \\
1 & surprise-n, dismay-n, outrage-n, & stupefaction.n.01, surprise.n.02 \\  & disbelief-n, horror-n & \\ \hline  & anger-n, frustration-n, fear-n, confusion-n, & emotion.n.01, mortal\_sin.n.01, \\
2 & anxiety-n, panic-n, uncertainty-n, doubt-n & emotional\_arousal.n.01, \\ \hline  & hatred-n, hate-n, bigtory-n, intolerance-n, & emotion.n.01, intolerance.n.02, \\
3 & contempt-n, prejudice-n, racism-n & impatience.n.03 \\ \hline  & resentment-n, bitterness-n, regret-n, & hostility.n.03, taste\_property.n.01, \\
4 & jealousy-n, envy-n & taste.n.01, disagreeableness.n.02 \\ \hline  & sadness-n, grief-n, rage-n, despair-n, & feeling.n.01, unhappiness.n.02, \\
5 & sorrow-n & uncheerfulness.n.02 \\ \hline \hline \end{tabular}
\end{table} 
Table 7: Best-ranked WordNet hypernym labels for associated communities FoF[_and_\(|or|_{i}^{anger}\)_ of the source lexeme _anger-n_ in English Timestamped newsfeed 2014–2019 corpus extracted through the \(F[hyper]_{anger}\) hypernym graph.