Measuring information-based energy and temperature of literary texts

Chang, Mei Chu; Yang, Albert C.; Stanley, H. Eugene; Peng, Chung-Kang

doi:10.1016/j.physa.2016.11.106

Cited by 10 publications

(8 citation statements)

References 20 publications

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“…Their analyses confirmed that there was a correlation between the global statistical properties of texts and the two categories of authors. Chang et al [ 32 ] analysed Shakespeare’s and Jin Yong’s works using a metric called “information-based energy”. They showed that the more popular works had a higher “energy”.…”

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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“…Their analyses revealed that poems have a higher complexity than plays and Shakepeare's work is interestingly more homogeneous than that of his contemporaries and is exceptionally close to the average use of words in that time period. Chang et al (2017) defined the information-based energy, combined from the relative temperature and information Shannon entropy, to quantify text complexity and an author's performance. Applying this method to texts of an English and an Chinese author, Shakespeare and Jin Yong, they showed that their more popular works have higher information-based energy.…”

Section: Entropy-based Methodsmentioning

confidence: 99%

Comparative Computational Analysis of Global Structure in Canonical, Non-Canonical and Non-Literary Texts

Mohseni,

Gast,

Redies

2020

Preprint

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This study investigates global properties of literary and non-literary texts. Within the literary texts, a distinction is made between canonical and non-canonical works. The central hypothesis of the study is that the three text types (non-literary, literary/canonical and literary/non-canonical) exhibit systematic differences with respect to structural design features as correlates of aesthetic responses in readers. To investigate these differences, we compiled a corpus containing texts of the three categories of interest, the Jena Textual Aesthetics 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 in chunks of text, and (iv) the distribution of topic probabilities in chunks of texts. These basic observations are grouped into two more general categories, (a) the low-level properties (i) and (ii), which are observed at the level of the sentence (reflecting linguistic decoding), and (b) the high-level properties (iii) and (iv), which are observed at the textual level (reflecting comprehension). The basic observations are transformed into time series, and these time series are subject to multifractal detrended fluctuation analysis (MFDFA), giving rise to three statistics: (i) the degree of fractality, (ii) the fractal dimension (width of the fractal spectrum), and (iii) the degree of asymmetry of the fractal spectrum. Our results show that low-level properties of texts are better discriminators than high-level properties, for the three text types under analysis. Canonical literary texts differ from non-canonical ones primarily in terms of variability. Fractality seems to be a universal feature of text, more pronounced in non-literary than in literary texts. While some of our results are hard to interpret from a literary point of view, we surmise that our findings reflect an important design feature of text, the distribution of discourse modes (Narrative, Report, Description, Information, Argument). Beyond the specific results of the study, we intend to open up new perspectives on the experimental study of textual aesthetics.

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“…all 884,647 words in 118, 406 lines, is a congruent expression of a worldview rendering all human affairs, excepting those serving the basic survival needs, both superfluous (see the epigraph to this chapter) and devoid of significance. It appears that exceptionally tight action coordination in the plot of Macbeth combined with succinct expressions of the Brain Functional Architecture and Human Understanding DOI: http://dx.doi.org /10.5772/intechopen.95594 author's worldview consistent with other such expressions throughout the corpus have surfaced in the text features detected by statistical measures [17].…”

Section: Understanding Shakespearementioning

confidence: 98%

Brain Functional Architecture and Human Understanding

Yufik¹

2021

Connectivity and Functional Specialization in the Brain

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The opening line in Aristotle’s Metaphysics asserts that “humans desire to understand”, establishing understanding as the defining characteristic of the human mind and human species. What is understanding and what role does it play in cognition, what advantages does it confer, what brain mechanisms are involved? The Webster’s Dictionary defines understanding as “apprehending general relations in a multitude of particulars.” A proposal discussed in this chapter defines understanding as a form of active inference in self-adaptive systems seeking to expand their inference domains while minimizing metabolic costs incurred in the expansions. Under the same proposal, understanding is viewed as an advanced adaptive mechanism involving self-directed construction of mental models establishing relations between domain entities. Understanding complements learning and serves to overcome the inertia of learned behavior when conditions are unfamiliar or deviate from those experienced in the past. While learning is common across all animals, understanding is unique to the human species. This chapter will unpack these notions, focusing on different facets of understanding. The proposal formulates hypotheses regarding the underlying neuronal mechanisms, attempting to assess their plausibility and reconcile them with the recent ideas and findings concerning brain functional architecture.

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Measuring information-based energy and temperature of literary texts

Cited by 10 publications

References 20 publications

Approximate Entropy in Canonical and Non-Canonical Fiction

Approximate Entropy in Canonical and Non-Canonical Fiction

Comparative Computational Analysis of Global Structure in Canonical, Non-Canonical and Non-Literary Texts

Brain Functional Architecture and Human Understanding

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