TY - JOUR
T1 - Recurrence networks in natural languages
AU - Baeza-Blancas, Edgar
AU - Obregón-Quintana, Bibiana
AU - Hernández-Gómez, Candelario
AU - Gómez-Meléndez, Domingo
AU - Aguilar-Velázquez, Daniel
AU - Liebovitch, Larry S.
AU - Guzmán-Vargas, Lev
N1 - Publisher Copyright:
© 2019 by the authors.
PY - 2019/5
Y1 - 2019/5
N2 - 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.
AB - 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.
KW - Natural languages
KW - Patterns repetition
KW - Recurrence networks
UR - http://www.scopus.com/inward/record.url?scp=85066620610&partnerID=8YFLogxK
U2 - 10.3390/e21050517
DO - 10.3390/e21050517
M3 - Artículo
AN - SCOPUS:85066620610
SN - 1099-4300
VL - 21
JO - Entropy
JF - Entropy
IS - 5
M1 - 517
ER -