Language trees and zipping.
In this Letter we present a very general method for extracting information from a generic string of characters, e.g., a text, a DNA sequence, or a time series. Based on data-compression techniques, its key point is the computation of a suitable measure of the remoteness of two bodies of knowledge. We present the implementation of the method to linguistic motivated problems, featuring highly accurate results for language recognition, authorship attribution, and language classification.
Benedetto
Dario
D
La Sapienza University, Mathematics Department, Piazzale Aldo Moro 5, 00185 Rome, Italy. benedetto@mat.uniroma1.it
Caglioti
Emanuele
E
Loreto
Vittorio
V
D000465:Algorithms; D004247:DNA / Q000235:genetics*; D007802:Language; D008962:Models, Theoretical; D010363:Pattern Recognition, Automated
D004247:DNA