Invariants and variability of synonymy networks: Self mediated agreement by confluence
Résumé
Edges of graphs that model real data can be
seen as judgements whether pairs of objects
are in relation with each other or not. So,
one can evaluate the similarity of two graphs
with a measure of agreement between judges
classifying pairs of vertices into two cate-
gories (connected or not connected). When
applied to synonymy networks, such measures
demonstrate a surprisingly low agreement be-
tween various resources of the same language.
This seems to suggest that the judgements
on synonymy of lexemes of the same lexi-
con radically differ from one dictionary ed-
itor to another. In fact, even a strong dis-
agreement between edges does not necessarily
mean that graphs model a completely differ-
ent reality: although their edges seem to dis-
agree, synonymy resources may, at a coarser
grain level, outline similar semantics. To in-
vestigate this hypothesis, we relied on shared
common properties of real world data net-
works to look at the graphs at a more global
level by using random walks. They enabled
us to reveal a much better agreement between
dense zones than between edges of synonymy
graphs. These results suggest that although
synonymy resources may disagree at the level
of judgements on single pairs of words, they
may nevertheless convey an essentially simi-
lar semantic information.
Domaines
LinguistiqueOrigine | Fichiers produits par l'(les) auteur(s) |
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