ABSTRACT Zipf’s law, Zipf-Mandelbrot law and Heaps’ law have been validated across languages and are viewed as universal linguistic principles. Recent studies increasingly investigated their parameter implications. However, their applicability to ancient languages remains underexplored, and the exponent of Heaps’ law has received limited attention. Our study explores how well these laws hold in Classical Chinese and whether their exponents can serve as diachronic indicators of lexical diversity. The results indicate that Classical Chinese exhibits distributional patterns consistent with the laws. The exponent of Zipf’s law decreases diachronically, whereas that of Heaps’ law and lexical diversity increase. The exponent of Zipf’s law correlates negatively with lexical diversity, that of Heaps’ law positively, and all the three show strong pairwise correlations. The parameters of Zipf-Mandelbrot law exhibit no clear monotonic trend and correlate only internally. Our findings provide support for the three laws in Classical Chinese and demonstrate both the exponents of Zipf’s law and Heaps’ law function as diachronic indicators of lexical diversity in Classical Chinese. However, the study is limited by its language scope, metric choice, missing polysemy analysis, untested mechanisms and unit-related issues. Future research could further extend these points.

ABSTRACT This study addresses the calculation and evaluation of syntactic distance, which is a quantitative measure of structural similarity or divergence between languages. Building on existing alignment-based, feature-based and data-driven approaches, we introduce a novel hypergraph-based metric that assesses syntactic distance through structural alignment while explicitly incorporating word order features. The approach is then applied to a multilingual parallel corpus annotated within the Universal Dependencies (UD) framework, yielding syntactic distances between English and 19 non-English languages. Empirical evaluation further demonstrates the robustness and effectiveness of the proposed measure. Compared with approaches that ablate the hypergraph formalism, ignore word order or rely solely on data-driven metrics, the new metric proves robust under random sampling variation and effectively captures syntactic distance: statistical analyses show that intra-group language pairs exhibit significantly shorter syntactic distances than inter-group pairs. This approach thus provides a novel, formally grounded perspective on language distance based purely on structural properties.

ABSTRACT This study investigates the writing styles of identical twins to determine if their shared genetics result in identical writing. We used computational authorship analysis techniques on writing samples from three sets of identical twins, focusing on lexical, grammatical, and syntactic features. Our results show that machine learning can effectively distinguish between the writing styles of identical twins, primarily using function words, part of speech tags or dependency relations as features. We also found that twins’ writing styles are not necessarily more similar to each other than to unrelated individuals. While the sample is too small to make generalizable conclusions, these findings potentially highlight the complex interplay of genetics and environment in shaping writing style, with implications for authorship attribution, forensic linguistics, and our understanding of human individuality.

ABSTRACT We introduce a data-centric hypothesis-testing framework to quantify the influence of sequentially correlated literary properties – such as thematic continuity – on textual classification tasks. Our method models label sequences as stochastic processes and uses an empirical autocovariance matrix to generate surrogate labelings that preserve sequential dependencies. This enables statistical testing to determine whether classification outcomes are primarily driven by thematic structure or by non-sequential features like authorial style. Applying this framework to a diverse corpus of English prose, we compare traditional (word n-grams and character k-mers) and neural (contrastively trained) embeddings in both supervised and unsupervised settings. Crucially, our method identifies when classifications are confounded by sequentially correlated similarity, showing that supervised and neural models are more prone to false positives – mistaking shared themes or cross-genre differences for stylistic signals. In contrast, unsupervised models using traditional features often yield high true positive rates with minimal false positives, especially in genre-consistent settings. By disentangling sequential from non-sequential influences, our approach provides a principled way to assess classification reliability. This is particularly impactful for authorship attribution, forensic linguistics, and the analysis of redacted or composite texts. Controlling for sequential correlation is essential for reducing false positives and ensuring classification outcomes reflect genuine stylistic distinctions.

ABSTRACT Language is a human-driven complex system. Previous quantitative research has focused extensively on the linguistic patterns and laws of this complex system’s lexical and syntactic aspects, but few studies have paid attention to linguistic patterns at the discourse level. This study investigates the probability distributions of the ranks and frequencies of Mongolian discourse markers (DMs) and those of DM functions extracted from a spoken corpus of twenty texts. It explores whether the rank-frequency distributions of DMs and DM functions can be modelled by the Zipf-Alekseev distribution in a corpus and separate texts, respectively. The results show that the rank-frequency distribution of DM satisfies the Zipf-Alekseev law at both the corpus and text levels. Likewise, the rank-frequency distribution of DM functions conforms to the right truncated modified Zipf-Alekseev distribution as well. The findings suggest that Zipf’s law also exhibits universality at the discourse level of spoken Mongolian. This study might shed light on further explorations of linguistic universals at different aspects of various human language systems.

ABSTRACT This study argues that corpus analysis has yet to incorporate probabilistic reasoning into the measurement of dispersion. This proposal continues a recent observation that some commonly used dispersion measures were impaired by an extreme sensitivity to the random noise characterizing any real-world data set. This article will show that this could have been avoided if (a) the underlying metaphor for ‘corpus dispersion’ had been more carefully considered and (b) the measures had been derived from an appropriate probability mass distribution. The text then proposes a computationally efficient dispersion measure developed from a Poisson model of an unbiased word dispersion. Unlike several current measures, this measure is shown to give a score that is meaningful at all word frequencies while being more sensitive to real dispersional bias and less sensitive to noise.

ABSTRACT Emojis in digital communication have drawn increasing academic attention. Qualitative studies mainly rely on a presumption that emojis share similar properties with units of natural language. It remains to be explored with quantitative methods whether emojis exhibit the same or similar behaviour from linguistic units (like words, morphemes). This study investigates emoji features in relation to language properties based on Zipf’s law and linear regression models. Results show that, firstly, the rank frequency distribution of emojis can be well fitted by Zipf’s law, and the parameters of emoji distribution are closer to those of written language. Secondly, most emoji categories tend to occur in the latter half of the tweet; while in some cases, they can also be at the beginning or in the middle of a tweet. Thirdly, the relative position of the more frequently-used emojis will be further back in the tweet. When emojis’ frequencies are relatively greater, their categories vary more in terms of their positions. In general, our quantitative findings suggest that emojis display linguistic properties to some extent. Our exploratory study demonstrates the value of applying linguistic laws and quantitative methods to investigate emoji features, extending the application of quantitative linguistic methods into emoji studies.

ABSTRACT According to the Menzerath-Altmann law, longer language constructs consist, on average, of shorter constituents. It is most often studied at the level of words and syllables (the mean syllable length gets shorter with the increasing word length). Its validity at this level was corroborated in several languages. However, it was claimed that Chinese is an exception with respect to the validity of the Menzerath-Altmann law. We show that the law is valid if word types are considered, while the behaviour of word tokens is different. This difference can be explained by the fact that the Zipf law of abbreviation is valid not only for words but also for syllables (shorter syllables are used more frequently).