Due to the absence of an objective methodology and empirical research on measuring cohesion and coherence in Turkish, this study aims to establish a framework for the objective assessment of textual coherence and cohesion in this agglutinative language. To achieve this, the coherence, cohesion, and comprehensibility of 25 texts were first evaluated by 60 experts in language education. Next, structural text features, NLP-based similarity models, and large language models with potential to determine coherence and cohesion were identified and applied to the texts. The analyses highlighted the factors that can be employed in evaluating coherence and cohesion, and regression equations were constructed using these variables. Consequently, for the first time, objective and empirical methodologies have been developed to assess the coherence and cohesion of Turkish texts, while accounting for the structural characteristics of agglutinative languages.
This corpus-based multifactorial study aims to explore potential predictors of repetition versus lexical variety in translation of repeated reporting verbs from English into Slovak in literary novels. First, we provide a theoretical overview of research on repetition and reporting verbs in Slovak and Czech translation studies. Next, using a sample of 14 literary novels extracted from InterCorp corpus (v.15), we fit multiple negative binomial regression models with mixed effects to assess the effect that several predictor variables (frequency, semantic category, verb length, number of senses, translators) have on the response variable, i.e. the number of Slovak target-text reporting verbs an English source-text (ST) reporting verb is translated into. The findings revealed that factors such as frequency of use of ST reporting verbs, the semantic category of neutral ST reporting verbs, as well as the translators as a random effect, influence Slovak translators’ decisions of using a wide variety of Slovak reporting verbs instead of preserving the originals’ patterns of repetition. More precisely, the model allowed us to explain 70% of the variation (per conditional r-squared) in the response variable. Against the backdrop of prevailing stylistic norms in Slovak, the findings shed light on the translator’s choices in rendering recurrent reporting verbs introducing direct speech, a stylistically salient feature of literary texts.
Zipf's law and similar frequency laws have been studied in many languages, but their behavior in Uzbek has not been investigated. In this paper, we examine the fit of the generalized Karlin model of Zipf's law to Uzbek texts. For 386 texts consisting of three different genres (prose, poetry, and newspapers), we compute the T_n and H_n statistics and their analogs in the first half of the text and propose a new goodness-of-fit statistic Q_n based on their joint asymptotic behaviour. Our results show that model fit varies systematically with text length and genre: newspapers and poetry, which are typically shorter and more thematically compact, fit the model significantly better than long narrative prose. These results clarify how the Karlin model works in Uzbek texts and provide an empirical baseline for future comparative studies of understudied languages, including agglutinative ones. We also compare the new statistic with two previously proposed tests, based on type and hapax, on ten English reference texts. The results show that Q_n produces virtually identical p-values to the second test and leads to the same accept/reject decisions at standard significance levels, while the first test is systematically more conservative.
This paper investigates syntactic complexity across eight genres in the works of Czech writer Karel & Ccaron;apek using a stylometric approach. The aim of the study is to determine whether syntactic complexity differs among the following genres: novels, short stories, travelogues, poems, newspaper columns, academic essays, children's literature, and personal correspondence. For the analysis, we compute five core complexity metrics: Average Sentence Length (in words and in clauses), Average Clause Length, Mean Dependency Distance (MDD), and Mean Hierarchical Distance (MHD). The results indicate systematic genre-specific differences in syntactic patterning within & Ccaron;apek's literary work. Academic literature and travelogues consistently occupy the upper end of the complexity scale, whereas novels and short stories show comparatively low complexity, in line with their narrative and accessibility-oriented functions. Poetry is characterised by relatively low average complexity but high variability, and children's literature emerges as unexpectedly complex across several metrics. Personal correspondence and newspaper columns take up intermediate positions. Overall, the findings demonstrate that syntactic complexity offers a useful structural signal for distinguishing genres within a single author and underscore the value of syntactic features in stylometric and genre-oriented research.
Text complexity assessment is an important applied problem that lacks a comprehensive solution. Studies vary in the text corpora used, the features analyzed, the analysis algorithms applied, and the assessment methods employed. We present a new methodology for complexity assessment, developed based on a representative collection of Russian-language school textbooks compiled by the authors. Textual complexity is represented numerically by a textbook's target class grade. First, we evaluate the average number of words per sentence and the average number of syllables per word for their ability to predict text complexity and implement machine learning methods, including linear regression and neural networks. We confirm the linear Flesch-Kincaid complexity formula in the sense that it cannot be improved upon. The influence of text segmentation on the assessment results is also examined, and several approaches to utilizing such information are presented in the paper. This approach gave us approximately 10% improvement in the R2 metric compared to the baseline model. We also examine a total of 47 linguistic parameters as predictors of complexity and evaluate their significance.
