Recurrence Networks in Natural Languages

Abstract

We present a study of natural language using the recurrence network method. In our approach, the repetition of patterns of characters is evaluated without considering the word structure in written texts from different natural languages. Our dataset comprises 85 ebookseBooks written in 17 different European languages. The similarity between patterns of length m is determined by the Hamming distance and a value r is considered to define a matching between two patterns, i.e., a repetition is defined if the Hamming distance is equal or less than the given threshold value r. In this way, we calculate the adjacency matrix, where a connection between two nodes exists when a matching occurs. Next, the recurrence network is constructed for the texts and some representative network metrics are calculated. Our results show that average values of network density, clustering, and assortativity are larger than their corresponding shuffled versions, while for metrics like such as closeness, both original and random sequences exhibit similar values. Moreover, our calculations show similar average values for density among languages which that belong to the same linguistic family. In addition, the application of a linear discriminant analysis leads to well-separated clusters of family languages based on based on the network-density properties. Finally, we discuss our results in the context of the general characteristics of written texts.

Comparing phonological and orthographic networks: A multiplex analysis

“…Many studies focused on the complexity of natural language have pointed out that language is the manifestation of different levels of complex organization [ 1 – 4 ], ranging from semantics [ 5 ] to syntax [ 6 , 7 ] or even emotional components [ 8 ]. Of particular interest are the applications of network science on language organization, where these levels of complexity may be explored by means of single [ 9 , 10 ] and multilayer graphs [ 11 , 12 ]. A number of studies have reported emergent organizational properties in language based on associations of semantics, orthographic similarities [ 13 ] and phonetics [ 14 , 15 ].…”

A multiplex analysis of phonological and orthographic networks

“…In some previous studies, the levels of complexity have been evaluated in terms of modeling based on complex single-layer networks or their extension to multilayer networks [19][20][21]. Of particular interest are findings about emergent organizational properties that encompass facets of language ranging from semantics to phonetics, including the written structure of language [22][23][24][25]. In many of these network-based approaches, it has been found that the behavior of the connectives -the number of neighbors of a given node-is often described by distributions that lie between power-law a1111111111 a1111111111 a1111111111 a1111111111 a1111111111 and narrow exponential behavior, depending on the language and the association criterion.…”