Quantifying origin and character of long-range correlations in narrative texts

Dróżdż, S.; Oświęcimka, Paweł; Kulig, Andrzej; Kwapień, Jarosław; Bazarnik, Katarzyna; Grabska-Gradzińska, Iwona; Rybicki, Jan; Stanuszek, Marek

doi:10.1016/j.ins.2015.10.023

Cited by 78 publications

(60 citation statements)

References 49 publications

Supporting

Mentioning

Contrasting

Unclassified

Order By: Relevance

“…For comparison with the particular cases considered below, representative instances of real multifractals having diverse properties are firstly presented, based on analyses conducted over the range q ∈ [−4, 4] [61]. To this end, two mathematical multifractals are considered, namely the binomial cascade and the chaotic metronome derived from the Ikeda map [62], together with several real-world time-series: the inter-beat intervals extracted from electrocardiographic signals (103885 data points), the sentence length variability of the "Finnegans Wake" book by James Joyce, the logarithmic returns of the American stock market index S&P500 (7440 data points), and the sunspot number variability (43495 data points) [11,21,26,35,49]. In all these cases, the multifractal spectrum f (α G ) assumes the shape of a wide inverted parabola, spanning ∆α G > 0.2, indicating a multifractal organization of the data (Fig.…”

Section: Examples Of Truly Multifractal Time-seriesmentioning

confidence: 99%

Wavelet-based discrimination of isolated singularities masquerading as multifractals in detrended fluctuation analyses

et al. 2020

Self Cite

View full text Add to dashboard Cite

The robustness of two widespread multifractal analysis methods, one based on detrended fluctuation analysis and one on wavelet leaders, is discussed in the context of time-series containing non-uniform structures with only isolated singularities. Signals generated by simulated and experimentallyrealized chaos generators, together with synthetic data addressing particular aspects, are taken into consideration. The results reveal essential limitations affecting the ability of both methods to correctly infer the non-multifractal nature of signals devoid of a cascade-like hierarchy of singularities. Namely, signals harboring only isolated singularities are found to artefactually give rise to broad multifractal spectra, resembling those expected in the presence of a well-developed underlying multifractal structure. Hence, there is a real risk of incorrectly inferring multifractality due to isolated singularities. The careful consideration of local scaling properties and the distribution of Hölder exponent obtained, for example, through wavelet analysis, is indispensable for rigorously assessing the presence or absence of multifractality.

show abstract

Section: Examples Of Truly Multifractal Time-seriesmentioning

confidence: 99%

Wavelet-based discrimination of isolated singularities masquerading as multifractals in detrended fluctuation analyses

et al. 2020

Self Cite

View full text Add to dashboard Cite

show abstract

“…Therefore, mapping a text into a time series of sentence lengths is a natural way to investigate text structures. Recent methods applied to study texts at sentence level include probability distributions [24,25,26,27] and correlations [26,27,17,28]. In recent studies, sentence length analysis have been related to style and authorship [29,26,27].…”

Section: Introductionmentioning

confidence: 99%

Robustness of sentence length measures in written texts

Vieira

Picoli

Mendes

2018

Physica A: Statistical Mechanics and its Applications

View full text Add to dashboard Cite

Hidden structural patterns in written texts have been subject of considerable research in the last decades. In particular, mapping a text into a time series of sentence lengths is a natural way to investigate text structure. Typically, sentence length have been quantified by using measures based on the number of words and the number of characters, but other variations are possible. To quantify the robustness of different sentence length measures, we analyzed a database containing about five hundred books in English. For each book, we extracted six distinct measures of sentence length, including number of words and number of characters (taking into account lemmatization and stop words removal). We compared these six measures for each book by using i ) Pearson's coefficient to investigate linear correlations; ii ) Kolmogorov-Smirnov test to compare distributions; and iii ) detrended fluctuation analysis (DFA) to quantify auto-correlations. We have found that all six measures exhibit very similar behavior, suggesting that sentence length is a robust measure related to text structure.

show abstract

“…That is, word length has been investigated in terms of word sequences, not individual words [15,18]. There are several methods to investigate into word length in sequences, including word length entropies [19,20] (Papadimitriou, 2010; Grotjahn, 1979), word length correlations [21,22], word length repetitions [23], and the latest word length motifs [15,18,24].…”

Section: Introductionmentioning

confidence: 99%

“…If word sequences in a text are mapped onto word length (measured in syllables) sequences, patterns may be found in the sequences of word lengths, which is reflected by word length correlation [28]. Furthermore, the patterns involve self-similarity among word length sequences, which can be estimated through word length correlations and explained by fractal and cascade effects in narrative texts [21].…”

Section: Introductionmentioning

confidence: 99%

Quantifying Evolution of Short and Long‐Range Correlations in Chinese Narrative Texts across 2000 Years

Chen

Liu

2018

Complexity

View full text Add to dashboard Cite

We investigate how short and long-range word length correlations evolve in Chinese narrative texts. The results show that, for short-range word length correlations, no significant linear evolutionary trend was found. But for long-range correlations, there are two opposite tendencies for two different regimes: the Hurst exponent of small-scale (box size ranges from 10 to 100) word length correlations decreases over time, and the exponent of large-scale (box size ranges from 101 to 1000) shows an increasing tendency. The increase of word length is corroborated as an essential regularity of word evolution in written Chinese. Further analyses show that a significant correlation coefficient is obtained between Hurst exponents from the small-scale correlations and mean word length across time. These indicate that word length correlation evolution possesses different self-adaptive mechanisms in terms of different scales of distances between words. We speculate that the increase of word length and sentence length in written Chinese may account for this phenomenon, in terms of both the social-cultural aspects and the self-adapting properties of language structures.

show abstract

Quantifying origin and character of long-range correlations in narrative texts

Cited by 78 publications

References 49 publications

Wavelet-based discrimination of isolated singularities masquerading as multifractals in detrended fluctuation analyses

Wavelet-based discrimination of isolated singularities masquerading as multifractals in detrended fluctuation analyses

Robustness of sentence length measures in written texts

Quantifying Evolution of Short and Long‐Range Correlations in Chinese Narrative Texts across 2000 Years

Contact Info

Product

Resources

About