P-leader multifractal analysis for text type identification

Abry, Patrice; Jaffard, Stéphane; Wendt, Herwig; Gournay, Lucie; Kyriacopoulou, Tita; Martineau, Claude; Martínez, César

doi:10.1109/icassp.2017.7953040

Cited by 4 publications

(1 citation statement)

References 22 publications

(35 reference statements)

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“…Although wavelet-based methods have been applied to a variety of fields, they have been rarely used in text processing. Leonarduzzi et al (2017) applied the wavelet p-leader method to the sentence-length series of novels that were written either for young people or adults. The authors showed that the latter category is more diverse in terms of its degree of multifractality.…”

Section: Wavelet-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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Section: Wavelet-based Methodsmentioning

confidence: 99%