dorsal/arxiv
View SchemaLanguage Time Series Analysis
| Authors | Kosmas Kosmidis, Alkiviadis Kalampokis, Panos Argyrakis |
|---|---|
| Categories | |
| ArXiv ID | physics/0607095 |
| URL | https://arxiv.org/abs/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.
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"abstract": "We use the Detrended Fluctuation Analysis (DFA) and the Grassberger-Proccacia\nanalysis (GP) methods in order to study language characteristics. Despite that\nwe construct our signals using only word lengths or word frequencies, excluding\nin this way huge amount of information from language, the application of\nGrassberger- Proccacia (GP) analysis indicates that linguistic signals may be\nconsidered as the manifestation of a complex system of high dimensionality,\ndifferent from random signals or systems of low dimensionality such as the\nearth climate. The DFA method is additionally able to distinguish a natural\nlanguage signal from a computer code signal. This last result may be useful in\nthe field of cryptography.",
"arxiv_id": "physics/0607095",
"authors": [
"Kosmas Kosmidis",
"Alkiviadis Kalampokis",
"Panos Argyrakis"
],
"categories": [
"physics.soc-ph"
],
"doi": "10.1016/j.physa.2006.02.042",
"title": "Language Time Series Analysis",
"url": "https://arxiv.org/abs/physics/0607095"
},
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