Evaluating the Irregularity of Natural Languages

Hernández-Gómez, Candelario; Basurto-Flores, Rogelio; Obregón-Quintana, Bibiana; Guzmán‐Vargas, L.

doi:10.3390/e19100521

Cited by 10 publications

(6 citation statements)

References 29 publications

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“…While entropy measures have mostly been used to analyse the distributional laws of linguistics, e.g., concerning word order [ 18 , 19 , 20 , 21 ] and word length [ 22 , 23 , 24 , 25 ], or for a comparison of languages in terms of ordering preferences and complexity [ 26 , 27 , 28 , 29 , 30 ], there are also studies that investigated the aesthetic preference and popularity of texts using entropy metrics.…”

Section: Introductionmentioning

confidence: 99%

Approximate Entropy in Canonical and Non-Canonical Fiction

Mohseni

Redies

Gast³

2022

Entropy

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Computational textual aesthetics aims at studying observable differences between aesthetic categories of text. We use Approximate Entropy to measure the (un)predictability in two aesthetic text categories, i.e., canonical fiction (`classics’) and non-canonical fiction (with lower prestige). Approximate Entropy is determined for series derived from sentence-length values and the distribution of part-of-speech-tags in windows of texts. For comparison, we also include a sample of non-fictional texts. Moreover, we use Shannon Entropy to estimate degrees of (un)predictability due to frequency distributions in the entire text. Our results show that the Approximate Entropy values can better differentiate canonical from non-canonical texts compared with Shannon Entropy, which is not true for the classification of fictional vs. expository prose. Canonical and non-canonical texts thus differ in sequential structure, while inter-genre differences are a matter of the overall distribution of local frequencies. We conclude that canonical fictional texts exhibit a higher degree of (sequential) unpredictability compared with non-canonical texts, corresponding to the popular assumption that they are more `demanding’ and `richer’. In using Approximate Entropy, we propose a new method for text classification in the context of computational textual aesthetics.

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Section: Introductionmentioning

confidence: 99%

Approximate Entropy in Canonical and Non-Canonical Fiction

Mohseni

Redies

Gast³

2022

Entropy

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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%

“…Our goal is to analyze the recurrence of patterns along the text, by examining the spatio-temporal organization of these patterns from a network science perspective. Recently, we reported the application of methods like such as the approximate entropy to the study of irregularities displayed by some natural languages [ 26 , 27 , 28 ]. In our approach, we consider the similarity between two patterns of length m based on based on the Hamming distance among them.…”

Section: Introductionmentioning

confidence: 99%

Recurrence Networks in Natural Languages

Baeza-Blancas

Obregón-Quintana

Hernández-Gómez³

et al. 2019

Entropy

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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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“…In van Cranenburgh et al ( 2019 ), summary statistics derived from topic modeling (Latent Dirichlet Allocation) and paragraph vectors are used to predict degrees of “literariness.” Maharjan et al ( 2017 ) explore a wide variety of features (including “readability”) that can be used to classify texts in terms of “likability.” Other standard methods of computational linguistics used in this context include sentiment and emotion analysis (Alm and Sproat, 2005 ; Francisco and Gervás, 2006 ; Kakkonen and Galić Kakkonen, 2011 ; Mohammad, 2011 ; Reagan et al, 2016 ; Maharjan et al, 2018 ). Global statistical properties such as complexity and entropy have been used to study the regularity (Mehri and Lashkari, 2016 ; Hernández-Gómez et al, 2017 ) and the quality of texts (Febres and Jaffe, 2017 ). Fractal analysis, which figures centrally in our study, has been applied to fictional texts as well (Drożdż and Oświȩcimka, 2015 ; Mehri and Lashkari, 2016 ; Chatzigeorgiou et al, 2017 ), and fractal patterns have been observed in both Western (Drożdż et al, 2016 ) and Chinese literature (Yang et al, 2016 ; Chen and Liu, 2018 ).…”

Section: Introductionmentioning

confidence: 99%

Fractality and Variability in Canonical and Non-Canonical English Fiction and in Non-Fictional Texts

Mohseni

Gast²,

Redies

2021

Front. Psychol.

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This study investigates global properties of three categories of English text: canonical fiction, non-canonical fiction, and non-fictional texts. The central hypothesis of the study is that there are systematic differences with respect to structural design features between canonical and non-canonical fiction, and between fictional and non-fictional texts. To investigate these differences, we compiled a corpus containing texts of the three categories of interest, the Jena Corpus of Expository and Fictional Prose (JEFP Corpus). Two aspects of global structure are investigated, variability and self-similar (fractal) patterns, which reflect long-range correlations along texts. We use four types of basic observations, (i) the frequency of POS-tags per sentence, (ii) sentence length, (iii) lexical diversity, and (iv) the distribution of topic probabilities in segments of texts. These basic observations are grouped into two more general categories, (a) the lower-level properties (i) and (ii), which are observed at the level of the sentence (reflecting linguistic decoding), and (b) the higher-level properties (iii) and (iv), which are observed at the textual level (reflecting comprehension/integration). The observations for each property are transformed into series, which are analyzed in terms of variance and subjected to Multi-Fractal Detrended Fluctuation Analysis (MFDFA), giving rise to three statistics: (i) the degree of fractality () of the fractal spectrum. The statistics thus obtained are compared individually across text categories and jointly fed into a classification model (Support Vector Machine). Our results show that there are in fact differences between the three text categories of interest. In general, lower-level text properties are better discriminators than higher-level text properties. Canonical fictional texts differ from non-canonical ones primarily in terms of variability in lower-level text properties. Fractality seems to be a universal feature of text, slightly more pronounced in non-fictional than in fictional texts. On the basis of our results obtained on the basis of corpus data we point out some avenues for future research leading toward a more comprehensive analysis of textual aesthetics, e.g., using experimental methodologies.

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Evaluating the Irregularity of Natural Languages

Cited by 10 publications

References 29 publications

Approximate Entropy in Canonical and Non-Canonical Fiction

Approximate Entropy in Canonical and Non-Canonical Fiction

Recurrence Networks in Natural Languages

Fractality and Variability in Canonical and Non-Canonical English Fiction and in Non-Fictional Texts

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