4 research outputs found
Complex Networks Measures for Differentiation between Normal and Shuffled Croatian Texts
This paper studies the properties of the Croatian texts via complex networks.
We present network properties of normal and shuffled Croatian texts for
different shuffling principles: on the sentence level and on the text level. In
both experiments we preserved the vocabulary size, word and sentence frequency
distributions. Additionally, in the first shuffling approach we preserved the
sentence structure of the text and the number of words per sentence. Obtained
results showed that degree rank distributions exhibit no substantial deviation
in shuffled networks, and strength rank distributions are preserved due to the
same word frequencies. Therefore, standard approach to study the structure of
linguistic co-occurrence networks showed no clear difference among the
topologies of normal and shuffled texts. Finally, we showed that the in- and
out- selectivity values from shuffled texts are constantly below selectivity
values calculated from normal texts. Our results corroborate that the node
selectivity measure can capture structural differences between original and
shuffled Croatian texts
Multilayer Network of Language: a Unified Framework for Structural Analysis of Linguistic Subsystems
Recently, the focus of complex networks research has shifted from the
analysis of isolated properties of a system toward a more realistic modeling of
multiple phenomena - multilayer networks. Motivated by the prosperity of
multilayer approach in social, transport or trade systems, we propose the
introduction of multilayer networks for language. The multilayer network of
language is a unified framework for modeling linguistic subsystems and their
structural properties enabling the exploration of their mutual interactions.
Various aspects of natural language systems can be represented as complex
networks, whose vertices depict linguistic units, while links model their
relations. The multilayer network of language is defined by three aspects: the
network construction principle, the linguistic subsystem and the language of
interest. More precisely, we construct a word-level (syntax, co-occurrence and
its shuffled counterpart) and a subword level (syllables and graphemes) network
layers, from five variations of original text (in the modeled language). The
obtained results suggest that there are substantial differences between the
networks structures of different language subsystems, which are hidden during
the exploration of an isolated layer. The word-level layers share structural
properties regardless of the language (e.g. Croatian or English), while the
syllabic subword level expresses more language dependent structural properties.
The preserved weighted overlap quantifies the similarity of word-level layers
in weighted and directed networks. Moreover, the analysis of motifs reveals a
close topological structure of the syntactic and syllabic layers for both
languages. The findings corroborate that the multilayer network framework is a
powerful, consistent and systematic approach to model several linguistic
subsystems simultaneously and hence to provide a more unified view on language