  We study the self-organization of the consonant inventories through a complex
network approach. We observe that the distribution of occurrence as well as
cooccurrence of the consonants across languages follow a power-law behavior.
The co-occurrence network of consonants exhibits a high clustering coefficient.
We propose four novel synthesis models for these networks (each of which is a
refinement of the earlier) so as to successively match with higher accuracy (a)
the above mentioned topological properties as well as (b) the linguistic
property of feature economy exhibited by the consonant inventories. We conclude
by arguing that a possible interpretation of this mechanism of network growth
is the process of child language acquisition. Such models essentially increase
our understanding of the structure of languages that is influenced by their
evolutionary dynamics and this, in turn, can be extremely useful for building
future NLP applications.
