Language Time Series Analysis
arXiv:physics/0607095 · doi:10.1016/j.physa.2006.02.042
Abstract
We use the Detrended Fluctuation Analysis (DFA) and the Grassberger-Proccacia analysis (GP) methods in order to study language characteristics. Despite that we construct our signals using only word lengths or word frequencies, excluding in this way huge amount of information from language, the application of Grassberger- Proccacia (GP) analysis indicates that linguistic signals may be considered as the manifestation of a complex system of high dimensionality, different from random signals or systems of low dimensionality such as the earth climate. The DFA method is additionally able to distinguish a natural language signal from a computer code signal. This last result may be useful in the field of cryptography.
21 pages, 5 figures, accepted in Physica A
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- Evolution of Vocabulary on Scale-free and Random Networks
- Equilibrium (Zipf) and Dynamic (Grasseberg-Procaccia) method based analyses of human texts. A comparison of natural (english) and artificial (esperanto) languages
- Punctuation effects in English and Esperanto texts
- Do Neural Nets Learn Statistical Laws behind Natural Language?
- Communication activity in social networks: growth and correlations
- Multifractal hopscotch in "Hopscotch" by Julio Cortazar
- Punctuation patterns in "Finnegans Wake" by James Joyce are largely translation-invariant
- Microbial genome as a fluctuating system: Distribution and correlation of coding sequence lengths
- A Random Matrix Approach to Language Acquisition
- A Comparison of natural (english) and artificial (esperanto) languages. A Multifractal method based analysis