Patterns in the English language: phonological networks, percolation and assembly models

Abstract

Abstract. In this paper we provide a quantitative framework for the study of phonological networks (PNs) for the English language by carrying out principled comparisons to null models, either based on site percolation, randomization techniques, or network growth models. In contrast to previous work, we mainly focus on null models that reproduce lower order characteristics of the empirical data. We find that artificial networks matching connectivity properties of the English PN are exceedingly rare: this leads to the hypothesis that the word repertoire might have been assembled over time by preferentially introducing new words which are small modifications of old words. Our null models are able to explain the "power-law-like" part of the degree distributions and generally retrieve qualitative features of the PN such as high clustering, high assortativity coefficient, and small-world characteristics. However, the detailed comparison to expectations from null models also points out significant differences, suggesting the presence of additional constraints in word assembly. Key constraints we identify are the avoidance of large degrees, the avoidance of triadic closure, and the avoidance of large non-percolating clusters.

Mental Lexicon Growth Modelling Reveals the Multiplexity of the English Language

“…Table 1 reports some network metrics [18] for the individual layers and the aggregated semantic multiplex, compared to configuration models with the same degree distributions. All the three layers display the small-world feature, in agreement with previous results [17,22]. The current literature suggests that small-worldness might be related to language robustness to individual word retrieval failure (e.g.…”

“…It is noteworthy that the phonological layer displays a network diameter almost three times larger than the mean path distance. Since its configuration model (CM) counterpart does not reproduce such pattern, this is an indication of a strong core-periphery structure within the network [22]. Further, all the individual layers are disconnected and have a giant component (GC).…”

“…Indeed, because of these different topological features we will keep these layers distinct within our linguistic multiplex. Assortative mixing is also strongly present at the phonological level, but note that in this case high assortativity is a feature inherited from the embedding space of phonological networks [22,21]. Configuration model aggregates are formed by aggregating the individual configuration model layers.…”

“…Experimental evidence showed that the resulting network degree and local clustering coefficient both correlated positively with speech errors and word identification times, indicating that also the topological properties of a word in the phonological network plays an important role in its cognitive processing [26]. In [22,21], we checked that artificial corpora, made of uncorrelated random words, could not reproduce specific features of the English phonological network. By means of percolation experiments we showed that the real PN actually inherits some features (e.g.…”

Cohort and Rhyme Priming Emerge from the Multiplex Network Structure of the Mental Lexicon

“…Network science provided language scientists with quantitative ways of representing and analysing the structure of lexical items within the mental lexicon [1,4,12,22]. For instance, concepts such as percolation techniques were used for detecting patterns of word confusability in phonology [12,22], strategies of language learning in healthy and clinical populations of children [6,23], differences in the levels of creativity of individual healthy subjects [3,11], or differences in the production of words in people with aphasia [17,18] or Alzheimer's disease [24]. However, the above studies considered only one aspect of language for establishing similarities among words, e.g., building single-layer networks including only phonological similarities among words [12].…”