Word-Length Correlations and Memory in Large Texts: A Visibility Network Analysis

Guzmán‐Vargas, L.; Obregón-Quintana, Bibiana; Aguilar-Velázquez, Daniel; Hernández-Pérez, R.; Liebovitch, Larry S.

doi:10.3390/e17117798

Cited by 14 publications

(12 citation statements)

References 33 publications

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“…Next, we calculated several network metrics for the texts from different languages. As we stated above, in our calculations we considered 5 segments with

15,000 elements and we set

, which is a value that roughly corresponds to the mean word length in several languages [ 26 , 36 , 37 , 38 ]. The threshold error value is set to

.…”

Section: Resultsmentioning

confidence: 99%

Recurrence Networks in Natural Languages

Baeza-Blancas

Obregón-Quintana

Hernández-Gómez³

et al. 2019

Entropy

Self Cite

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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.

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“…Next, we calculated several network metrics for the texts from different languages. As we stated above, in our calculations we considered 5 segments with

15,000 elements and we set

, which is a value that roughly corresponds to the mean word length in several languages [ 26 , 36 , 37 , 38 ]. The threshold error value is set to

.…”

Section: Resultsmentioning

confidence: 99%

Recurrence Networks in Natural Languages

Baeza-Blancas

Obregón-Quintana

Hernández-Gómez³

et al. 2019

Entropy

Self Cite

View full text Add to dashboard Cite

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“…The NVGs were introduced by Lacasa et al [44] and they permitted us to give a visual representation of time series in the plane. This approach has been extensively used in different areas such as economics [45,46], linguistics [47], meteorology [48], and seismology [49,50]. Moreover, NVGs have been successfully applied to diagnose Alzheimer's disease [51,52] and epilepsy [53], as well as to the classification of sleep stages [54].…”

Section: Methods (Natural Visibility Graph)mentioning

confidence: 99%

Power-Law Distribution of Natural Visibility Graphs from Reaction Times Series

2019

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In this study, we analyze the response times of students to yes/no decision tasks from the perspective of network science. We analyze the properties of the natural visibility graphs (NVG) associated with their reaction time series. We observe that the degree distribution of these graphs usually fits a power-law distribution px = x-α. We study the range in which parameter α occurs and the changes of this exponent with respect to the age and gender of the students. In addition to this, we also study the links between the parameter α and the parameters of the ex-Gaussian distribution that best fit the response times for each subject.

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“…Several papers have used similar methods to map texts into token length sequences [24,[56][57][58][59]. The token length sequence mapping method (Eq.…”

Section: Token Length Sequence (Tls)mentioning

confidence: 99%

Enriching feature engineering for short text samples by language time series analysis

2020

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In this case study, we are extending feature engineering approaches for short text samples by integrating techniques which have been introduced in the context of time series classification and signal processing. The general idea of the presented feature engineering approach is to tokenize the text samples under consideration and map each token to a number, which measures a specific property of the token. Consequently, each text sample becomes a language time series, which is generated from consecutively emitted tokens, and time is represented by the position of the respective token within the text sample. The resulting language time series can be characterised by collections of established time series feature extraction algorithms from time series analysis and signal processing. This approach maps each text sample (irrespective of its original length) to 3970 stylometric features, which can be analysed with standard statistical learning methodologies. The proposed feature engineering technique for short text data is applied to two different corpora: the Federalist Papers data set and the Spooky Books data set. We demonstrate that the extracted language time series features can be successfully combined with standard machine learning approaches for natural language processing and have the potential to improve the classification performance. Furthermore, the suggested feature engineering approach can be used for visualizing differences and commonalities of stylometric features. The presented framework models the systematic feature engineering based on approaches from time series classification and develops a statistical testing methodology for multi-classification problems.

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Word-Length Correlations and Memory in Large Texts: A Visibility Network Analysis

Cited by 14 publications

References 33 publications

Recurrence Networks in Natural Languages

Recurrence Networks in Natural Languages

Power-Law Distribution of Natural Visibility Graphs from Reaction Times Series

Enriching feature engineering for short text samples by language time series analysis

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